Electronic device including plurality of cameras, method, and computer-readable storage medium

Abstract

This electronic device may comprise: a memory storing instructions and including one or more storage media; a first camera having a first field of view and a second camera having a second field of view, the second camera (110) and the first camera (105) being disposed on the same side of the electronic device in the same direction; a display; and at least one processor including a processing circuit, wherein the instructions, when executed individually or collectively by the at least one processor, cause the electronic device to: on the basis of receiving an input for acquiring an image by using the first camera, control the first camera and the second camera to acquire a first image and a second image; acquire a third image by compensating for a peripheral area of the acquired first image by using the acquired second image; and store the third image in the memory.

Description

Electronic device, method, and computer-readable storage medium including multiple cameras

[0001] The present disclosure relates to an electronic device, a method, and a non-transitory computer-readable storage medium including a plurality of cameras.

[0002] An electronic device may include multiple cameras. For example, the multiple cameras may have different fields of view (FOVs). For example, the electronic device may acquire images using the multiple cameras.

[0003] The above information is presented solely as background information to aid understanding of the present disclosure. No claim or determination is made as to whether any of the above is applicable as prior art with respect to the present disclosure.

[0004] According to an aspect of the present disclosure, an electronic device is provided. The electronic device may include at least one processor comprising a memory and a processing circuit, the memory storing instructions and including one or more storage media. The electronic device may include at least one processor comprising a first camera having a first field of view (FOV), a second camera having a second angle of view wider than the first FOV, the second camera and the first camera being arranged on the same side of the electronic device facing the same direction, a display, and a processing circuit. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to receive an input for acquiring an image through the first camera. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to control the first camera to acquire a first image based on the input. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to control the second camera to acquire a second image based on the input. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to acquire a third image by compensating for a peripheral area of ​​the acquired first image using the acquired second image. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to store the third image in the memory.

[0005] According to an aspect of the present disclosure, a method is described. The method may be performed within an electronic device, the electronic device comprising a memory storing instructions and one or more storage media, a first camera having a first field of view (FOV), and a second camera having a second field of view wider than the first FOV, the second camera and the first camera being positioned on the same side of the electronic device facing the same direction, and a display. The method may include receiving an input for acquiring an image through the first camera. The method may include controlling the first camera to acquire a first image based on the input. The method may include controlling the second camera to acquire a second image based on the input. The method may include acquiring a third image by compensating for a peripheral area of ​​the acquired first image using the acquired second image. The method may include storing the third image in the memory.

[0006] According to an aspect of the present disclosure, a non-transitory computer-readable storage medium is described. The non-transitory computer-readable storage medium may store one or more programs. The one or more programs may include instructions that, when executed by an electronic device, cause the electronic device to receive an input for acquiring an image through the first camera, the electronic device including a memory storing instructions and one or more storage media, a first camera having a first field of view (FOV), a second camera having a second field of view wider than the first FOV, the second camera and the first camera being arranged on the same side of the electronic device and facing the same direction, and a display. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to control the first camera to acquire a first image based on the input. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to control the second camera to acquire a second image based on the input. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to acquire a third image by compensating for a peripheral area of ​​the acquired first image using the acquired second image. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to store the third image in the memory.

[0007] Other aspects, advantages, and salient features of the present disclosure will become apparent to those skilled in the art from the following detailed description of various embodiments of the present disclosure taken in conjunction with the accompanying drawings.

[0008] The above and other aspects, features, and advantages of specific embodiments of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings.

[0009] Figure 1 illustrates an example of acquiring images using multiple cameras.

[0010] Figure 2 illustrates an example of deteriorated characteristics caused in the peripheral area of ​​an image acquired using a camera.

[0011] Figure 3 is a simplified block diagram of an electronic device.

[0012] FIG. 4 is a flowchart illustrating exemplary operations of an electronic device for obtaining an image with compensated deteriorated characteristics.

[0013] FIG. 5a illustrates an example of a partial image of a second image corresponding to a peripheral area of ​​a first image including an edge of the first image.

[0014] Figure 5b shows a chart representing the change in distortion amount according to the area of ​​the lens.

[0015] Figure 5c shows a chart representing the change in the peripheral light ratio according to the area of ​​the lens.

[0016] Figure 6 shows an example of running the model to obtain an image with compensated degraded features.

[0017] Figure 7 shows an example of an image in which degraded characteristics have been compensated.

[0018] FIG. 8 is a flowchart illustrating exemplary operations of an electronic device for obtaining a set of multiple images with compensated deteriorated characteristics.

[0019] FIG. 9a illustrates an example of a set of multiple images acquired using a first camera and a partial image of an image acquired using a second camera.

[0020] Figure 9b illustrates an example of running the model to obtain a set of multiple images with compensated degraded features.

[0021] FIG. 10 is a flowchart illustrating exemplary operations of an electronic device for acquiring a video including an image with compensated deteriorated characteristics.

[0022] FIG. 11a illustrates examples of partial images of a set of multiple images acquired using a first camera and a set of multiple images acquired using a second camera.

[0023] Figure 11b shows an example of running the model to obtain a video containing an image with compensated degraded features.

[0024] Figure 12 illustrates an example of displaying a preview image with compensated deteriorated characteristics.

[0025] Figure 13 illustrates an example of user input for switching modes.

[0026] Figure 14 shows an example of obtaining an image with improved image quality.

[0027] FIG. 15 is a block diagram of an electronic device within a network environment according to various embodiments.

[0028] FIG. 16 is a block diagram illustrating a camera module according to various embodiments.

[0029] Figure 17 is a schematic diagram of an exemplary AI system.

[0030] Aspects of the present disclosure address at least the problems and / or disadvantages mentioned above and / or provide at least the advantages described below. Accordingly, aspects of the present disclosure provide an electronic device, a method, and one or more non-transitory computer-readable storage media including a plurality of cameras.

[0031] Additional aspects will be partly explained in the following description, partly apparent from the above description, or may be learned by practicing the embodiments presented.

[0032] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings so that those skilled in the art can easily implement the present disclosure. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described herein. In connection with the description of the drawings, the same or similar reference numerals may be used for identical or similar components. Furthermore, in the drawings and related descriptions, descriptions of well-known functions and configurations may be omitted for clarity and conciseness.

[0033] The terms and words used in the following description and claims are used by the inventors solely in their bibliographical meanings. Accordingly, it will be apparent to those skilled in the art that the following description of various embodiments of the present disclosure is provided for illustrative purposes only and is not intended to limit the present disclosure, which is defined by the appended claims and their equivalents.

[0034] The singular forms "a and an" and "the" are to be understood to include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a component surface" includes reference to one or more of these surfaces.

[0035] It should be clear that the blocks and combinations of flowcharts within each flowchart can be performed by one or more computer programs containing computer-executable instructions. The one or more computer programs may be stored entirely in a single memory device, or the one or more computer programs may be divided into different portions stored in different memory devices.

[0036] Any of the functions or operations described herein may be processed by a single processor or a combination of processors. The single processor or the combination of processors is a circuit that performs processing and includes an application processor (AP) (e.g., a central processing unit (CPU)), a communication processor (CP) (e.g., a modem), a graphic processing unit (GPU), a neural processing unit (NPU) (e.g., an artificial intelligence (AI) chip), a WI-FI chip, a Bluetooth chip, a global positioning system (GPS) chip, a near field communication (NFC) chip, connectivity chips, a sensor controller, a fingerprint sensor controller, a display driver integrated circuit, an audio CODEC chip, a universal serial bus (USB) controller, a camera controller, an image processing IC, a microprocessor unit (MPU), a system on a chip (SoC), an IC, or a circuit like the same.

[0037] Figure 1 illustrates an example of acquiring images using multiple cameras.

[0038] Referring to FIG. 1, the electronic device (100) may be described as a device available for acquiring an image. For example, the electronic device (100) may be one of various forms of mobile devices, such as smartphones (e.g., bar-type smartphones, foldable-type smartphones, or rollable-type smartphones), tablets, wearable devices, cellular phones, laptops, and / or other similar computing devices, having various form factors that include circuits (or circuitry) for providing an operation for acquiring an image.

[0039] For example, the electronic device (100) may include a plurality of cameras (e.g., a first camera (105) and a second camera (110)). For example, the plurality of cameras may be used to acquire images. For example, the first camera (105) may be positioned on one side of the electronic device (100). For example, the second camera (110) may be positioned on one side of the electronic device (100) where the first camera (105) is positioned, facing the direction in which the first camera (105) faces. For example, the first camera (105) and the second camera (110) may be positioned vertically adjacent to one side of the electronic device (100), or, although not illustrated in FIG. 1, the first camera (105) and the second camera (110) may be positioned leftwardly adjacent to one side of the electronic device (100).

[0040] For example, the first camera (105) may be described as a wide-angle camera. For example, the second camera (110) may be described as an ultrawide-angle camera. For example, the first camera (105) and the second camera (110) may include cameras having different angles of view, other than the wide-angle camera and the ultrawide-angle camera, or cameras having different degraded characteristics (e.g., distortion) for the same area within the images acquired from each of the first camera (105) and the second camera (110). For example, the first camera (105) may have a first field of view (FOV) (125). For example, the second camera (110) may have a second angle of view (130). For example, the second angle of view (130) may be wider than the first angle of view (125). For example, the second camera (110) may be positioned on one side of the electronic device (100) where the first camera (105) is positioned, facing the direction in which the first camera (105) is facing, so that the first angle of view (125) may overlap the second angle of view (130). For example, the second camera (110) and the first camera (105) may be positioned on the same side of the electronic device (100) facing the same direction.

[0041] For example, state (115) can be described as a state in which a user (120) photographs a subject (135). For example, within state (115), when the electronic device (100) photographs a subject (135) using the first camera (105) and the second camera (110), it can acquire multiple images of the subject (135).

[0042] For example, the electronic device (100) can obtain a first image using a first camera (105). For example, the electronic device (100) can obtain a second image using a second camera (110). For example, the first camera (105) includes a first lens having a first angle of view (125) that is narrower than a second angle of view (130) and overlaps within the second angle of view (130), so that a scene expressed in the first image can be included in a scene expressed in the second image.

[0043] For example, an image acquired using a wide-angle camera (e.g., the first camera (105)) or an ultra-wide-angle camera (e.g., the second camera (110)) may include degraded characteristics. Degraded characteristics included in an image acquired using a wide-angle camera (e.g., the first camera (105)) or an ultra-wide-angle camera (e.g., the second camera (110)) are exemplified in the description of FIG. 2.

[0044] Figure 2 illustrates an example of deteriorated characteristics caused in the peripheral area of ​​an image acquired using a camera.

[0045] Referring to FIG. 2, a deteriorated feature may be included in an image (200) acquired using a wide-angle camera (e.g., the first camera (105)). For example, the deteriorated feature in the image (200) may be caused within a peripheral area (210-1, 210-2) of the image (200) that includes the edge of the image (200).

[0046] For example, the deteriorated characteristics may be caused by refraction of light entering from the outside through the first lens of the first camera (105). For example, since the refraction of light increases toward the edge of the lens, the peripheral region (210-1, 210-2) of the image (200) including the edge of the image (200) may have more deteriorated characteristics than the central region (205) of the image (200). For example, the deteriorated characteristics of the image (200) may include distortion of the image (200), characteristics due to a relatively low peripheral light ratio, and / or relatively low quality of the image (200).

[0047] For example, a subject (215) (e.g., subject (135) of FIG. 1) located within a peripheral region (210-1, 210-2) of an image (200) including an edge of the image (200) may appear stretched or dark to the user due to distortion caused within the peripheral region (210-1, 210-2) of the image (200) including an edge of the image (200). For example, the user may feel uncomfortable because the subject (215) located within a peripheral region (210-1, 210-2) of the image (200) including an edge of the image (200) appears stretched or dark. For example, a method for relieving the user's discomfort caused by distortion occurring at the edge of the image (200) may be required.

[0048] For example, to resolve this inconvenience, the electronic device (100) can compensate for distortion caused within the peripheral region (210-1, 210-2) of the image (200) including the edge of the image (200). For example, an image acquired using the second camera (110) can be used to compensate for distortion caused within the peripheral region (210-1, 210-2) of the image (200) including the edge of the image (200). For example, the electronic device (100) can change or reduce distortion within the peripheral region (210-1, 210-2) of the image (200) using the image acquired using the second camera (110).

[0049] The electronic device (100) may perform operations exemplified in the description of FIGS. 4 to 13 to compensate for distortion caused within a peripheral region (210-1, 210-2) of the image (200) including the edge of the image (200). The electronic device (100) may include components for performing the above operations. The components may be exemplified in the description of FIG. 3.

[0050] Figure 3 is a simplified block diagram of an electronic device.

[0051] Referring to FIG. 3, the electronic device (100) may be one of various forms of mobile devices, such as smartphones having various form factors (e.g., bar-type smartphones, foldable-type smartphones, or rollable-type smartphones), tablets, wearable devices, cellular phones, laptops, and / or other similar computing devices. For example, the electronic device (100) may include the electronic device (100) of FIG. 1 or may correspond to the electronic device (100) of FIG. 1. For example, the electronic device (100) may include at least a portion of the electronic device (1401) of FIG. 14 or may correspond to at least a portion of the electronic device (1401) of FIG. 14. For example, the electronic device (100) may include at least one processor (300), a memory (310), a display (320), a first camera (105), and a second camera (110).

[0052] At least one processor (300) may include processing circuitry. For example, at least one processor (300) may include a central processing unit (CPU) (e.g., including processing circuitry). For example, at least one processor (300) may include a graphic processing unit (GPU) (e.g., including processing circuitry) and / or a neural processing unit (NPU) (e.g., including processing circuitry). For example, at least one processor (300) may be described as an application processor. For example, at least one processor (300) may be configured to control the memory (310), the display (320), the first camera (105), and the second camera (110). At least one processor (300) may be configured to individually or collectively execute instructions stored in the memory (310) to cause the electronic device (100) to perform at least some of the operations illustrated in the descriptions of FIGS. 1 and 2 . At least one processor (300) may be configured to execute instructions stored in the memory (310) to cause the electronic device (100) to perform at least some of the operations illustrated in the descriptions of FIGS. 4 through 14.

[0053] The memory (310) may include one or more storage media. For example, the memory (310) may store various data used by at least one component of the electronic device (100) (e.g., at least one processor (300), the display (320), the first camera (105), and / or the second camera (110)). For example, the data may include input data or output data for software and commands related thereto. The memory (310) may include volatile memory or non-volatile memory.

[0054] The display (320) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the strength of the force generated by the touch. For example, the display (320) may be configured to display a preview image. For example, the display (230) may be configured to receive an input for acquiring an image using the first camera (105) while the preview image is displayed.

[0055] The first camera (105) may be configured to capture an image. For example, the first camera (105) may be described as a wide-angle camera. For example, the first camera (105) may have a first angle of view (e.g., the first angle of view (125) of FIG. 1 ). For example, the first camera (105) may be positioned at one side of the electronic device (100). As a non-limiting example, the first angle of view may include a diagonal field of view of the first camera (105).

[0056] The second camera (110) may be configured to capture an image. For example, the second camera (110) may be described as an ultrawide angle camera. For example, the second camera (110) may have a second angle of view (e.g., the second angle of view (130) of FIG. 1). For example, the second angle of view may be wider than the first angle of view. As a non-limiting example, the second angle of view may be at least 1.3 times wider than the first angle of view. As a non-limiting example, the second angle of view may include a diagonal angle of view of the second camera (110). For example, the second camera (110) may be positioned on one side of the electronic device (100) where the first camera (105) is positioned, facing the direction in which the first camera (105) faces. For example, the second camera (110) and the first camera (105) may be positioned on the same side of the electronic device facing the same direction.

[0057] The electronic device (100) illustrated in the description of FIG. 3 can execute at least some of the operations illustrated in the description of FIGS. 4 to 14. For example, the operations illustrated in the description of FIGS. 4 to 14 can be caused by (or within) the electronic device (100) under the control of at least one processor (300).

[0058] FIG. 4 is a flowchart illustrating exemplary operations of an electronic device for obtaining an image with compensated deteriorated characteristics.

[0059] Referring to FIG. 4, in operation 400, at least one processor (300) may display a preview image on the display (320). For example, the preview image may be acquired based on the first camera (105). For example, the preview image may be displayed to indicate an image to be acquired using the first camera (105) in response to an input for acquiring an image.

[0060] For example, at least one processor (300) may receive the input while displaying the preview image. For example, the input may include a user input requesting to execute the first camera (105) to display the preview image on the display (320) and to take a picture while the preview image is displayed. For example, the input may include a touch input for an executable object displayed overlapping the preview image (or together with the preview image). For example, the input may include a touch input of tapping the executable object. For example, the input may include a touch input having a contact point on the executable object. For example, the at least one processor (300) may identify the input through the display (320) (e.g., a touch screen). For example, the input may be provided by an input means (e.g., a button) of the electronic device (100) or by an input means of an external electronic device (e.g., a stylus pen, a headset, a smartwatch) connected to the electronic device (100).

[0061] For example, in operation 410, at least one processor (300) may control a first camera (105) to acquire a first image based on the input. For example, the first image may be described as an image acquired using the first camera (105) at the time the input is received. For example, at least one processor (300) may control a second camera (110) to acquire a second image based on the input. For example, the second camera (110) may be executed (or activated) based on the input while the first camera (105) is executed (or activated). For example, the second camera (110) may be executed (or activated) to compensate for deteriorated characteristics of an image acquired from the first camera (105). For example, the second image may be described as an image acquired using the second camera (110) at the time the input is received. For example, at least one processor (300) may execute (or activate) a second camera (110) to acquire a second image that is used to compensate for degraded characteristics of the first image.

[0062] Referring back to FIG. 1, within state (115), the user (120) can capture a subject (135) using the first camera (105) and the second camera (110) of the electronic device (100).

[0063] For example, the first image may have a first angle of view (125) by having a first camera (105) including a first lens having a first angle of view (125). For example, the first image may represent a first scene. For example, the first scene may represent at least a portion of the environment surrounding the electronic device (100) included within the first angle of view (125).

[0064] For example, the second image may have a second angle of view (130) by having a second camera (110) with a second lens having a second angle of view (130). For example, the second image may represent a second scene. For example, the second scene may represent at least a portion of the environment surrounding the electronic device (100) included within the second angle of view (130).

[0065] For example, since the first camera (105) and the second camera (110) are positioned facing substantially the same direction on one side of the electronic device (100), the first angle of view (125) can overlap at least partially with the second angle of view (130). For example, since the second angle of view (130) is larger than the first angle of view (125) and the first angle of view (125) overlaps at least partially with the second angle of view (130), the second scene can include the first scene. For example, since the second scene includes the first scene, the second image can include a portion corresponding to the first image.

[0066] Referring again to FIG. 4, at operation 420, at least one processor (300) may identify a peripheral region of the first image that includes an edge of the first image. For example, the peripheral region of the first image may be predetermined based on a first angle of view of the first lens (e.g., the first angle of view (125) of FIG. 1). For example, a deteriorated characteristic may be induced within the peripheral region of the first image that includes an edge of the first image.

[0067] For example, at least one processor (300) may identify a partial image of the second image corresponding to a peripheral area of ​​the first image including an edge of the first image based on acquiring the second image. Identifying the partial image of the second image is exemplified in the description of FIG. 5A.

[0068] FIG. 5a illustrates an example of a partial image of a second image corresponding to a peripheral area of ​​a first image including an edge of the first image.

[0069] Referring to FIG. 5A, at least one processor (300) can identify a peripheral region (510-1, 510-2) of the first image (500) that includes an edge of the first image (500). For example, within the peripheral region (510-1, 510-2) of the first image (500) that includes an edge of the first image (500), a more degraded characteristic may occur than within the central region (505) of the first image (500). For example, within FIG. 5A, the peripheral region (510-1, 510-2) of the first image (500) is illustrated as two regions, but the peripheral region (510-1, 510-2) of the first image (500) may be a single region in which the two regions are connected.

[0070] For example, the deteriorated characteristics of the first image (500) may include characteristics deteriorated by barrel distortion, deteriorated characteristics related to gradation, and / or deteriorated characteristics related to relative illumination. For example, the deteriorated characteristics may be caused by refraction of light entering from the outside through the first lens of the first camera (105). For example, the refraction of light increases toward the edge of the lens, so that the peripheral region (510-1, 510-2) of the first image (500) including the edge of the first image (500) may be more distorted than the central region (505) of the first image (500).

[0071] For example, a subject (515) expressed within a peripheral area (510-1, 510-2) of the first image (500) including the edge of the first image (500) may be stretched or darkened depending on the deteriorated characteristic.

[0072] For example, the second image (520) may include a portion (525) corresponding to the first image (500). For example, since the second image (520) includes a portion (525) corresponding to the first image (500), the second image (520) may include partial images (530-1, 530-2) of the second image (520) that correspond to the peripheral areas (510-1, 510-2) of the first image (500) within the second image (520).

[0073] For example, at least one processor (300) may identify a partial image (530-1, 530-2) of the second image (520) based on acquiring the second image (520). For example, the partial image (530-1, 530-2) of the second image (520) may be located away from an edge of the second image (520) within the second image (520). For example, the partial image (530-1, 530-2) of the second image (520) may be located close to (or within) a central region of the second image (520). For example, since the partial images (530-1, 530-2) of the second image (520) are positioned away from the edge of the second image (520), the partial images (530-1, 530-2) of the second image (520) may have relatively less degraded characteristics. For example, the partial images (530-1, 530-2) of the second image (520) may have higher grayscale performance than the peripheral region (510-1, 510-2) of the first image (500). For example, since the partial images (530-1, 530-2) of the second image (520) have higher grayscale performance than the peripheral region (510-1, 510-2) of the first image (500), continuous color or brightness can be expressed within the partial images (530-1, 530-2) of the second image (520). For example, a subject (535) expressed within a partial image (530-1, 530-2) of a second image (520) may be less stretched or less darkened than a subject (515) expressed within a peripheral area (510-1, 510-2) of a first image (500).

[0074] For example, since the first camera (105) has a first angle of view (e.g., the first angle of view (125) of FIG. 1) that is narrower than the second angle of view (e.g., the second angle of view (130) of FIG. 1), the first camera (105) may have a smaller F number than the second camera (110) that includes the second lens. For example, since the first camera (105) has a relatively small F number, it may have a relatively large amount of light. For example, since the second camera (110) has a relatively large F number, when the aperture of the second camera (110) is opened to the maximum, the second image (520) may have a relatively low resolution value due to spherical aberration of the second lens of the second camera (110). For example, since the second camera (110) has a relatively large F value, when the aperture of the second camera (110) is opened to the minimum, diffraction of light may occur, and the second image (520) may have a relatively low resolution value. For example, since the first camera (105) has a smaller F value than the second camera (110), the first image (500) may have a higher resolution value than the second image (520).

[0075] For example, the first camera (105) may have a larger optical format than the second camera (110). For example, if the first camera (105) has a larger optical format than the second camera (110), and the first camera (105) has a first angle of view that is narrower than the second angle of view, the first image (500) may have a higher resolution value than the second image (520).

[0076] For example, the second image (520) may include a partial region (540) corresponding to the central region (505) of the first image (500). For example, since the first image (500) has a higher resolution value than the second image (520), the central region (505) of the first image (500) may have a higher resolution value than the partial region (540) of the second image (520). For example, since the central region (505) of the first image (500) includes relatively low deterioration characteristics, compensation for the deterioration caused in the central region (505) of the first image (500) may not be required within the electronic device (100).

[0077] For example, at least one processor (300) may use a partial image (530-1, 530-2) of a second image (520) to compensate for deteriorated characteristics caused in a peripheral area (510-1, 510-2) of a first image (500).

[0078] Figure 5b shows a chart representing the change in distortion amount according to the area of ​​the lens.

[0079] Referring to FIG. 5b, the chart (541) represents the change in distortion amount according to the area of ​​the lens. The horizontal axis (542) in the chart (541) represents the area of ​​the lens, and the vertical axis (543) in the chart (541) represents the degree of image distortion.

[0080] For example, the amount of distortion of the first image (500) acquired through the first camera (105) can be expressed as a line (544) in the chart (541). For example, the amount of distortion of the second image (520) acquired through the second camera (110) can be expressed as a line (545) in the chart (541).

[0081] For example, according to the line (544) in the chart (541), the first image (500) may have a greater degree of distortion in the peripheral region than in the central region. For example, the first image (500) may have a first amount of distortion (547) in a first region (546) within the first image (500). For example, the second image (520) may have a second amount of distortion (549) in a second region (548) within the second image (520). For example, the first amount of distortion (547) may be relatively greater than the second amount of distortion (549). As a non-limiting example, the amount of distortion in the peripheral region of the first image (500) may be 1.3 times or more the amount of distortion in the second image (520). As a non-limiting example, the amount of distortion in the peripheral region of the first image (500) may be 2.7 times or less the amount of distortion in the second image (520). But it is not limited to this.

[0082] For example, a scene represented by a first area (546) in a first image (500) may substantially correspond to a scene represented by a second area (548) in a second image (520). For example, at least one processor (300) may compensate for distortion using an area including a second area (548) in a second image (520) with respect to a surrounding area including the first area (546) in the first image (500).

[0083] Figure 5c shows a chart representing the change in the peripheral light ratio according to the area of ​​the lens.

[0084] Referring to FIG. 5c, the chart (550) represents the change in the peripheral light ratio according to the area of ​​the lens. The horizontal axis (551) in the chart (550) represents the area of ​​the lens, and the vertical axis (552) in the chart (550) represents the peripheral light ratio. For example, the peripheral light ratio can be defined as the ratio of the light incident on the center and edge of the camera. For example, as the rear focal length of the lens decreases, the peripheral light ratio may decrease. For example, a light source may be incident on the lens at a certain angle. For example, if the light source is incident on the lens at a certain angle, it may not converge on the photodiode of the camera. For example, if the light source is not able to converge on the photodiode of the camera, a decrease in the peripheral light ratio may occur.

[0085] As a non-limiting example, the ambient light ratio of the second image (520) may be at least 1.7 times the ambient light ratio of the surrounding area of ​​the first image (500). As a non-limiting example, the ambient light ratio of the second image (520) may be less than 3 times the ambient light ratio of the surrounding area of ​​the first image (500). However, this is not limiting.

[0086] For example, the ambient light ratio of the first image (500) acquired through the first camera (105) can be expressed as a line (553) in the chart (550). For example, the ambient light ratio of the second image (520) acquired through the second camera (110) can be expressed as a line (554) in the chart (550).

[0087] For example, according to a line (553) within the chart (550), the first image (500) may have a lower peripheral light ratio within a peripheral region than within a central region. For example, the first image (500) may have a first peripheral light ratio (556) within a first region (555) within the first image (500). For example, the second image (520) may have a second peripheral light ratio (558) within a second region (557) within the second image (520). For example, the first peripheral light ratio (556) may be relatively lower than the second peripheral light ratio (558).

[0088] For example, a scene represented by a first area (555) in a first image (500) may substantially correspond to a scene represented by a second area (557) in a second image (520). For example, at least one processor (300) may compensate for deteriorated characteristics of the first image (500) by a peripheral light ratio using an area including a second area (557) in a second image (520) with respect to a peripheral area including the first area (555) in the first image (500).

[0089] Referring back to FIG. 4, at operation 430, at least one processor (300) may execute a model using partial images (530-1, 530-2) of the first image (500) and the second image (520). For example, at least one processor (300) may execute the model by providing the partial images (530-1, 530-2) of the first image (500) and the second image (520) to the model. For example, at least one processor (300) may acquire a third image based on the execution of the model.

[0090] For example, at operation 440, at least one processor (300) may store a third image in memory (310) as a result of the input (e.g., the input in operation 400 of FIG. 4).

[0091] The operation of executing the above model to obtain the third image is illustrated within the description of Fig. 6.

[0092] Figure 6 shows an example of running the model to obtain an image with compensated degraded features.

[0093] Referring to FIG. 6, the electronic device (100) may include a model (600). For example, the model (600) may include a machine learning model, a deep learning model, and / or a generative artificial intelligence model. For example, the model (600) may be trained to compensate for deteriorated characteristics of the first image (500) caused by the first camera (105) in the peripheral areas (510-1, 510-2) of the first image (500) (e.g., deteriorated characteristics of the first image (500) in the description of FIG. 5A).

[0094] For example, at least one processor (300) can execute the model (600) using the first image (500) and a partial image (530) of the second image (e.g., the partial images (530-1, 530-2) of the second image (520) of FIG. 5A). For example, at least one processor (300) can execute the model (600) by providing the first image (500) and a partial image (530) of the second image to the model (600).

[0095] For example, at least one processor (300) may obtain a third image (610) based on the execution of the model (600). For example, at least one processor (300) may store the third image (610) in the memory (310) as a result of the input (e.g., the input in operation 400 of FIG. 4).

[0096] As a non-limiting example, the server may include a model (600). At least one processor (300) may transmit a first image (500) and a partial image (530) of a second image to the server. The server may receive the first image (500) and the partial image (530) of the second image from the electronic device (100). The server may execute the model (600) using the received first image (500) and the partial image (530) of the second image. The server may obtain a third image (610) based on the execution of the model (600). The server may transmit the third image (610) to the electronic device (100). At least one processor (300) may receive the third image (610) from the server. By receiving the third image (610) from the server, the at least one processor (300) may obtain the third image (610). But it is not limited to this.

[0097] As a non-limiting example, at least one processor (300) can input (or provide) a first image (500) and a second image (e.g., the second image (520) of FIG. 5A) to the model (600). For example, at least one processor (300) can execute the model (600) by inputting (or providing) the first image (500) and the second image (520) to the model (600).

[0098] For example, the model (600) can identify a portion (e.g., portion (525) of FIG. 5A) corresponding to the first image (500) within the second image (520). For example, the model (600) can obtain a third image (610) by compensating for deteriorated characteristics of the first image (500) based on identifying a portion corresponding to the first image (500) within the second image (520). For example, at least one processor (300) can obtain the third image (610) based on the execution of the model (600). However, the present invention is not limited thereto.

[0099] A model (600) according to an embodiment of the present disclosure may be an artificial neural network model including a plurality of layers and / or operations (or calculations). The model (600) according to an embodiment of the present disclosure may be one of a feedforward neural network (FNN), a deep neural network (DNN), a convolutional neural network (CNN), a region with convolution neural network (R-CNN), a region proposal network (RPN), a recurrent neural network (RNN), a stacking-based deep neural network (S-DNN), a state-space dynamic neural network (S-SDNN), a deconvolution network, a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), deep Q-networks, a fully convolutional network, a long short-term memory (LSTM) network, a classification network, or a combination of two or more of the above, but is not limited to the above examples. A model (600) according to one embodiment can be trained on specified data, acquire input data, and perform operations based on the input data to generate output data. In addition to a hardware structure, the model (600) may additionally or alternatively include a software structure.

[0100] For example, the third image (610) may include a partial image in which deteriorated characteristics caused in the peripheral area (510-1, 510-2) of the first image (500) are compensated. The third image (610) is exemplified in the description of FIG. 7.

[0101] Figure 7 shows an example of an image in which degraded characteristics have been compensated.

[0102] Referring to FIG. 7, the third image (610) may include peripheral regions (700-1, 700-2) of the third image (610) corresponding to the peripheral regions of the first image (500) (e.g., peripheral regions (510-1, 510-2) of FIG. 5A). For example, although the peripheral regions (700-1, 700-2) of the third image (610) are illustrated as two regions in FIG. 7, the two regions of the third image (610) may be a single region connected to each other. For example, the peripheral regions (700-1, 700-2) of the third image (610) may be described as regions in which deteriorated characteristics caused in the peripheral regions (510-1, 510-2) of the first image (500) are compensated for. For example, the peripheral area (700-1, 700-2) of the third image (610) can be determined using a partial image of the second image (e.g., the partial image (530) of the second image of FIG. 6) that has less deteriorated characteristics than the deteriorated characteristics caused within the peripheral area (510-1, 510-2) of the first image (500).

[0103] For example, the third image (610) may include a central region (705) of the third image (610) that corresponds to a central region of the first image (500) (e.g., the central region (505) of FIG. 5A). For example, the central region (705) of the third image (610) may be determined using a central region (505) of the first image (500) that has a higher resolution value than a partial region of the second image (520) (e.g., the partial region (540) of FIG. 5A).

[0104] As a non-limiting example, the third image (610) may be described as a composite image of the first image (500) and a partial image (530) of the second image. As a non-limiting example, at least one processor (300) may obtain the third image (610) by synthesizing the first image (500) and the partial image (530) of the second image such that the boundary between the first image (500) and the partial image (530) of the second image is connected based on the execution of a model (e.g., model (600) of FIG. 6). However, the present invention is not limited thereto.

[0105] For example, since the third image (610) is acquired based on a partial image (530) of the second image, the peripheral area (700-1, 700-2) of the third image (610) may have less deteriorated characteristics than the peripheral area (510-1, 510-2) of the first image (500). For example, since the peripheral area (700-1, 700-2) of the third image (610) has less deteriorated characteristics than the peripheral area (510-1, 510-2) of the first image (500), the subject (710) in the peripheral area (700-1, 700-2) of the third image (610) may be expressed as less stretched or less dark than the subject (e.g., the subject (515) of FIG. 5A) in the peripheral area (510-1, 510-2) of the first image (500).

[0106] For example, the central region (705) of the third image (610) may be obtained based on the central region (505) of the first image (500), and thus may have a higher resolution value than a partial region of the second image (e.g., a partial region (540) of the second image (520) of FIG. 5A).

[0107] For example, at least one processor (300) may acquire a first set of a plurality of images using a first camera (105) in a continuous mode. For example, the plurality of images of the first set may include a degraded characteristic caused within a peripheral area of ​​each of the plurality of images of the first set. For example, an operation for compensating for the degraded characteristic may be exemplified within the description of FIG. 8.

[0108] FIG. 8 is a flowchart illustrating exemplary operations of an electronic device for obtaining a set of multiple images with compensated deteriorated characteristics.

[0109] Referring to FIG. 8, in operation 800, at least one processor (300) may display a preview image within a continuous mode. For example, while the preview image is displayed within the continuous mode, at least one processor (300) may receive an input for acquiring an image using the first camera (105). For example, the input may refer to the input within operation 400 of FIG. 4.

[0110] For example, in operation 810, at least one processor (300) may control the first camera (105) to acquire a first set of a plurality of images based on the input received within the continuous mode. For example, the first set of the plurality of images may include a first image (e.g., the first image (500) of FIG. 5A). For example, the first set of the plurality of images may include images captured continuously. For example, the first set of the plurality of images may include images acquired using the first camera (105) until a reference time elapses from the time at which the input is received.

[0111] For example, at least one processor (300) may control a second camera (110) to acquire a second image (e.g., the second image (520) of FIG. 5A) based on the input received within the continuous mode.

[0112] For example, a plurality of images of a first set may have a first angle of view (125) because a first camera (105) has a first angle of view (e.g., a first angle of view (125) of FIG. 1). For example, a second image (520) may have a second angle of view (130) because a second camera (110) has a second angle of view (e.g., a second angle of view (130) of FIG. 1).

[0113] For example, the second angle of view (130) may be larger than the first angle of view (125), and the first angle of view (125) may overlap at least partially with the second angle of view (130), so that the second image (520) may include portions corresponding to the plurality of images of the first set.

[0114] For example, in operation 820, at least one processor (300) may identify a peripheral region of the first set of multiple images that includes edges of the first set of multiple images. For example, the peripheral region of the first set of multiple images may correspond to a peripheral region of the first image (500) (e.g., peripheral regions (510-1, 510-2) of the first image (500) of FIG. 5A). For example, a degraded characteristic may be caused within the peripheral region of the first set of multiple images that includes edges of the first set of multiple images.

[0115] For example, at least one processor (300) may identify a partial image of the second image (520) corresponding to a peripheral area of ​​the first set of multiple images including edges of the first set of multiple images (e.g., partial images (530-1, 530-2) of FIG. 5A) based on acquiring the second image (520).

[0116] For example, in operation 830, at least one processor (300) may execute a model (e.g., model (600) of FIG. 6) using a first set of multiple images and partial images (530-1, 530-2) of a second image (520). For example, at least one processor (300) may execute the model (600) by providing the first set of multiple images and partial images (530-1, 530-2) of the second image (520) to the model (600). For example, at least one processor (300) may obtain a second set of multiple images based on the execution of the model (600).

[0117] For example, in operation 840, at least one processor (300) may store a second set of multiple images in memory (310) as a result of inputting (e.g., inputting in operation 800 of FIG. 8). For example, the second set of multiple images may be stored in association with a third image (e.g., the third image (610) of FIG. 6). For example, at least one processor (300) may store a file including the second set of multiple images in memory (310).

[0118] A first set of multiple images used to run the model and a partial image (530) of the second image (520) are illustrated within the description of FIG. 9a.

[0119] FIG. 9a illustrates an example of a set of multiple images acquired using a first camera and a partial image of an image acquired using a second camera.

[0120] Referring to FIG. 9A, the first set (900) of multiple images may include a first image (900-1). For example, the first image (900-1) may correspond to the first image (500) of FIG. 5A. For example, the first image (900-1) may be described as an image acquired using the first camera (105) at the time the input is received within a continuous mode.

[0121] For example, a first set (900) of multiple images may include multiple images (900-1, 900-2, 900-N) that are acquired sequentially. For example, the multiple images of the first set (900) may include multiple images acquired from a time point at which an input is received until a reference time elapses within a continuous mode. For example, the multiple images (900-1, 900-2, 900-N) of the first set (900) may be at least partially different from each other. For example, since the multiple images (900-1, 900-2, 900-N) of the first set (900) are acquired sequentially, each of the multiple images (900-1, 900-2, 900-N) of the first set (900) may have a relatively small difference.

[0122] For example, a partial image (530) of a second image (e.g., a second image (520) of FIG. 5A) may correspond to a peripheral area of ​​a plurality of images (900-1, 900-2, 900-N) of a first set (900). For example, a first viewpoint of a scene expressed by a partial image (530) of a second image (520) may correspond to a first viewpoint of a scene expressed by a peripheral area of ​​a first image (900-1). For example, among the plurality of images (900-1, 900-2, 900-N) of a first set (900), images (900-2, 900-n) excluding the first image (900-1) may express scenes of second viewpoints after the first viewpoint. For example, since each of the plurality of images (900-1, 900-2, 900-N) of the first set (900) has a relatively small difference, the difference between the scenes of the first time point and the scenes of the second time points can be explained as being relatively small.

[0123] For example, since the difference between the scenes of the first time point and the scenes of the second time points is relatively small, obtaining the second set of the plurality of images using the first set (900) of the plurality of images and the partial image (530) of the second image (520) may not have an error. Executing the model (e.g., the model (600) of FIG. 6) to obtain the second set of the plurality of images is exemplified in the description of FIG. 9b.

[0124] Figure 9b illustrates an example of running the model to obtain a set of multiple images with compensated degraded features.

[0125] Referring to FIG. 9B, at least one processor (300) can execute a model (600) using a first set (900) of multiple images and a partial image (530) of a second image (520). For example, the model (600) can be described as the model (600) of FIG. 6. For example, at least one processor (300) can execute the model (600) by providing the model (600) with the first set (900) of multiple images and a partial image (530) of the second image (520).

[0126] For example, at least one processor (300) may obtain a second set (910) of multiple images based on the execution of the model (600). For example, at least one processor (300) may store the second set (910) of multiple images in the memory (310) as a result of the input (e.g., the input in operation 800 of FIG. 8).

[0127] For example, the second set (910) of the plurality of images may include partial images in which deteriorated characteristics caused in the peripheral area of ​​each of the plurality of images (900-1, 900-2, 900-N) of the first set (900) are compensated for.

[0128] For example, each of the plurality of images of the second set (910) may be referred to in the description of Fig. 7. For example, each of the plurality of images of the second set (910) may be described as images synthesized from each of the plurality of images (900-1, 900-2, 900-N) of the first set (900) and a partial image (530) of the second image.

[0129] For example, the peripheral areas of the plurality of images of the second set (910) may have less deteriorated characteristics than the peripheral areas of the plurality of images (900-1, 900-2, 900-N) of the first set (900) by being acquired based on the partial image (530) of the second image. For example, since the peripheral areas of the plurality of images of the second set (910) have less deteriorated characteristics than the peripheral areas of the plurality of images (900-1, 900-2, 900-N) of the first set (900), a subject within the peripheral areas of the plurality of images of the second set (910) may be expressed as less stretched or less dark than a subject within the peripheral areas of the plurality of images (900-1, 900-2, 900-N) of the first set (900). For example, the plurality of images of the second set (910) may be acquired based on the plurality of images (900-1, 900-2, 900-N) of the first set (900), and thus may have a higher resolution value than a partial region of the second image (520) (e.g., a partial region (540) of FIG. 5A).

[0130] For example, at least one processor (300) may acquire a first set of a plurality of images using a first camera (105) in a video mode. For example, the plurality of images of the first set may include a degraded characteristic caused within a peripheral area of ​​each of the plurality of images of the first set. For example, an operation for compensating for the degraded characteristic may be exemplified within the description of FIG. 10.

[0131] FIG. 10 is a flowchart illustrating exemplary operations of an electronic device for acquiring a video including an image with compensated deteriorated characteristics.

[0132] Referring to FIG. 10, in operation 1000, at least one processor (300) may display a preview image within a video mode. For example, while the preview image is being displayed within the video mode, the at least one processor (300) may receive an input. For example, the input may refer to the input within operation 400 of FIG. 4.

[0133] For example, in operation 1010, at least one processor (300) may control the first camera (105) to acquire a first set of a plurality of images based on the input received within the video mode. For example, the first set of the plurality of images may include a first image (e.g., the first image (500) of FIG. 5A). For example, the first set of the plurality of images may include images captured continuously. For example, the first set of the plurality of images may include images captured using the first camera (105) from a time point at which an input is received to a time point at which an input to stop capturing is received.

[0134] For example, at least one processor (300) may control the second camera (110) to acquire a second set of multiple images based on the input received within the video mode. For example, the second set of multiple images may include a second image (e.g., the second image (520) of FIG. 5A). For example, the second set of multiple images may include images captured continuously. For example, the second set of multiple images may include images captured using the second camera (110) from a time point at which an input is received to a time point at which an input to stop capturing is received.

[0135] For example, the plurality of images of the first set may have a first angle of view (125) because the first camera (105) has a first angle of view (e.g., the first angle of view (125) of FIG. 1). For example, the plurality of images of the second set may have a second angle of view (130) because the second camera (110) has a second angle of view (e.g., the second angle of view (130) of FIG. 1).

[0136] For example, the second angle of view (130) is wider than the first angle of view (125), and the first angle of view (125) overlaps at least partially with the second angle of view (130), so that the plurality of images of the second set can include portions corresponding to the plurality of images of the first set.

[0137] For example, in operation 1020, at least one processor (300) may identify a peripheral region of the first set of multiple images that includes edges of the first set of multiple images. For example, the peripheral region of the first set of multiple images may correspond to a peripheral region of the first image (500) (e.g., peripheral regions (510-1, 510-2) of the first image (500) of FIG. 5A). For example, the at least one processor (300) may cause a deteriorated characteristic to be caused within the peripheral region of the first set of multiple images that includes edges of the first set of multiple images.

[0138] For example, at least one processor (300) may identify partial images of the second set of multiple images corresponding to a peripheral area of ​​the first set of multiple images including an edge of the first set of multiple images, based on acquiring the second set of multiple images.

[0139] For example, in operation 1030, at least one processor (300) may execute a model (e.g., model (600) of FIG. 6) using partial images of a first set of multiple images and a second set of multiple images. For example, at least one processor (300) may execute the model (600) by providing the model (600) with partial images of the first set of multiple images and the second set of multiple images. For example, at least one processor (300) may obtain a video including a third set of multiple images based on the execution of the model.

[0140] For example, in operation 1040, at least one processor (300) may store, in memory (310), a video comprising a third set of multiple images as a result of input (e.g., input in operation 1000 of FIG. 10). Partial images of the first set of multiple images and the second set of multiple images used to execute the model are exemplified in the description of FIG. 11A.

[0141] FIG. 11a illustrates examples of partial images of a set of multiple images acquired using a first camera and a set of multiple images acquired using a second camera.

[0142] Referring to FIG. 11A, a first set (1100) of multiple images may include a first image (1100-1). For example, the first image (1100-1) may correspond to the first image (500) of FIG. 5A. For example, the first image (1100-1) may be described as an image acquired using the first camera (105) at the time of receiving the input within a video mode.

[0143] For example, a first set (1100) of multiple images may include multiple images (1100-1, 1100-2, 1100-N) sequentially acquired using a first camera (105). For example, the multiple images of the first set (1100) may include multiple images acquired from a point in time when an input is received to a point in time when an input to stop shooting is received, within a video mode.

[0144] For example, the partial images (1110) of the plurality of images of the second set may include a partial image (1110-1) of the second image. For example, the partial image (1110-1) of the second image may correspond to the partial images (530-1, 530-2) of the second image (520) of FIG. 5A. For example, each of the partial images (1110) of the plurality of images of the second set may correspond to a peripheral area of ​​each of the plurality of images (1100-1, 1100-2, 1100-N) of the first set (1100).

[0145] For example, the surrounding area of ​​each of the plurality of images (1100-1, 1100-2, 1100-N) of the first set (1100) may correspond to each of the partial images (1110) of the plurality of images of the second set. For example, the scene represented by the surrounding area of ​​the first image (1100-1) may correspond to the scene represented by the partial image (1110-1) of the second image. For example, the surrounding area of ​​one image (1100-N) of the plurality of images (1100-1, 1100-2, 1100-N) of the first set (1100) may correspond to one image (1110-N) of the partial images (1110) of the plurality of images of the second set. Executing a model (e.g., model (600) of FIG. 6) to obtain a video including a third set of the plurality of images is exemplified within the description of FIG. 11B.

[0146] Figure 11b shows an example of running the model to obtain a video containing an image with compensated degraded features.

[0147] Referring to FIG. 11B, at least one processor (300) can execute a model (600) using a first set (1100) of multiple images and partial images (1110) of a second set of multiple images. For example, the model (600) can be described as the model (600) of FIG. 6. For example, at least one processor (300) can execute the model (600) by providing the model (600) with the first set (1100) of multiple images and partial images (1110) of the second set of multiple images.

[0148] For example, at least one processor (300) may obtain a video (1120) comprising a third set of multiple images based on the execution of the model (600). For example, at least one processor (300) may store the video (1120) comprising the third set of multiple images in the memory (310) as a result of the input (e.g., the input in operation 1000 of FIG. 10).

[0149] For example, a third set of multiple images may include partial images in which deteriorated characteristics caused in the peripheral area of ​​each of the multiple images (1100-1, 1100-2, 1100-N) of the first set (1100) are compensated for.

[0150] For example, each of the plurality of images of the third set may be referred to in the description of FIG. 7. For example, each of the plurality of images of the third set may be described as images synthesized from each of the plurality of images (1100-1, 1100-2, 1100-N) of the first set (1100) and each of the partial images (1110) of the plurality of images (1110-1, 1110-2, 1110-N) of the second set.

[0151] For example, the peripheral areas of the plurality of images of the third set may be acquired based on partial images (1110) of the plurality of images of the second set, and thus may have less degraded characteristics than the peripheral areas of the plurality of images (1100-1, 1100-2, 1100-N) of the first set (1100). For example, the peripheral areas of the plurality of images of the third set may have less degraded characteristics than the peripheral areas of the plurality of images (1100-1, 1100-2, 1100-N) of the first set (1100), and thus, a subject within the peripheral areas of the plurality of images of the third set may be expressed as less stretched or less dark than a subject within the peripheral areas of the plurality of images (1100-1, 1100-2, 1100-N) of the first set (1100).

[0152] For example, the central region of the plurality of images of the third set may be obtained based on the central region of the plurality of images (1100-1, 1100-2, 1100-N) of the first set (1100), thereby having a higher resolution value than the partial region of the plurality of images of the second set (e.g., the partial region of the plurality of images of the second set corresponding to the partial region (540) of FIG. 5A).

[0153] For example, since the video (1120) includes a third set of multiple images, the surrounding areas of the frames of the video (1120) may have less degraded characteristics than the surrounding areas of the frames of the video that include the first set of multiple images (1100). For example, since the video (1120) includes a third set of multiple images, it may have a higher resolution value than the video that includes the second set of multiple images.

[0154] Figure 12 illustrates an example of displaying a preview image with compensated deteriorated characteristics.

[0155] Referring to FIG. 12, at least one processor (300) can compensate for deteriorated characteristics of a preview image displayed prior to receiving an input (e.g., an input within operation 400 of FIG. 4, an input within operation 800 of FIG. 8, and / or an input within operation 1000 of FIG. 10). For example, at least one processor (300) can control a first camera (105) to acquire a fourth image based on executing a software application (hereinafter, a camera software application) that provides functions for a camera. For example, the fourth image can have a first angle of view (125) because the first camera (105) has a first angle of view (e.g., the first angle of view (125) of FIG. 1). For example, the fourth image can correspond to the first image (500) of FIG. 5A. For example, the fourth image may have a second resolution that is lower than the first resolution of the first image (500).

[0156] For example, at least one processor (300) may control the second camera (110) to acquire a fifth image based on executing a camera software application. For example, the fifth image may have a second angle of view (130) because the second camera (110) has a second angle of view (e.g., the second angle of view (130) of FIG. 1). For example, the fifth image may correspond to the second image (520) of FIG. 5A. For example, the fifth image may have a fourth resolution that is lower than the third resolution of the second image (520).

[0157] For example, at least one processor (300) may identify a peripheral region of the fourth image including an edge of the fourth image. For example, the peripheral region of the fourth image may correspond to the peripheral regions (510-1, 510-2) of the first image (500) of FIG. 5A. For example, the peripheral region of the fourth image may be predetermined according to the first angle of view of the first camera (105). For example, a deteriorated characteristic may be caused within the peripheral region of the fourth image including an edge of the fourth image.

[0158] For example, at least one processor (300) may identify a partial image of the fifth image corresponding to a peripheral area of ​​the fourth image including an edge of the fourth image based on acquiring the fifth image. For example, the partial image of the fifth image may correspond to the partial images (530-1, 530-2) of the second image (520) of FIG. 5A. For example, the operation of identifying a partial image of the fifth image corresponding to a peripheral area of ​​the fourth image including an edge of the fourth image may refer to the description of FIG. 5A.

[0159] For example, at least one processor (300) can execute the model using partial images of the fourth image and the fifth image. For example, at least one processor (300) can execute the model by providing the partial images of the fourth image and the fifth image to the model. For example, at least one processor (300) can obtain a sixth image (1200) based on the execution of the model. For example, the sixth image (1200) can correspond to the third image (610) of FIG. 7. For example, the sixth image (1200) can have a sixth resolution that is lower than the fifth resolution of the third image (610). For example, the operation of obtaining the sixth image (1200) can refer to the description of FIG. 6.

[0160] For example, the sixth image (1200) may include a central region (1210) of the sixth image (1200) that corresponds to the central region of the fourth image. For example, the central region (1210) of the sixth image (1200) may be determined using a central region of the fourth image that has a higher resolution value than a resolution value of a partial region of the fifth image (e.g., a partial region (540) of FIG. 5A).

[0161] As a non-limiting example, the sixth image (1200) may be described as a composite image of partial images of the fourth image and the fifth image. As a non-limiting example, at least one processor (300) may obtain the sixth image (1200) by synthesizing partial images of the fourth image and the fifth image such that the boundary between the partial images of the fourth image and the fifth image is connected based on the execution of a model (e.g., model (600) of FIG. 6). However, the present invention is not limited thereto.

[0162] For example, the peripheral area (1215-1, 1215-2) of the sixth image (1200) may have less deteriorated characteristics than the peripheral area of ​​the fourth image, since it is acquired based on a partial image of the fifth image. For example, since the peripheral area (1215-1, 1215-2) of the sixth image (1200) has less deteriorated characteristics than the peripheral area of ​​the fourth image, the subject (1220) within the peripheral area (1215-1, 1215-2) of the sixth image (1200) may be expressed as less stretched or less dark than the subject within the peripheral area of ​​the fourth image (e.g., the subject (515) of FIG. 5A).

[0163] For example, the central region (1210) of the sixth image (1200) may have a higher resolution value than a partial region of the fifth image (e.g., a partial region (540) of FIG. 5A) by being acquired based on the central region of the fourth image.

[0164] For example, at least one processor (300) may display the sixth image (1200) through the display (320) based on acquiring the sixth image (1200). For example, at least one processor (300) may display the sixth image in place of the fourth image, thereby providing an image having less deteriorated characteristics than the surrounding area of ​​the fourth image.

[0165] For example, a camera software application may include multiple modes. For example, the camera software application may include a normal mode, a continuous mode, and / or a video mode. For example, the camera software application may further include a mode for acquiring a third image (e.g., the third image (610) of FIG. 6) based on an input. For example, a user input for switching from the normal mode (or the continuous mode, or the video mode) to the mode for acquiring the third image (610) is exemplified in the description of FIG. 13.

[0166] Figure 13 illustrates an example of user input for switching modes.

[0167] Referring to FIG. 13, a state (1300) may be described as a state in which the electronic device (100) is in a normal mode (or continuous mode, or video mode). For example, in the state (1300), at least one processor (300) may display a preview image (1315) through a display (320). For example, at least one processor (300) may display an executable object (1305) for switching modes overlapping the preview image (1315) (or next to an area in which the preview image is displayed).

[0168] For example, at least one processor (300) can receive an input (1310) for an executable object (1305). For example, the input (1310) for the executable object (1305) can include a touch input that taps the executable object (1305). For example, the input (1310) for the executable object (1305) can include a touch input having a point of contact on the executable object (1305). For example, the at least one processor (300) can identify a touch input for the executable object (1305) through a display (320) (e.g., a touchscreen).

[0169] For example, at least one processor (300) may switch from a normal mode (or a continuous mode, or a video mode) to a mode for acquiring a third image (e.g., the third image (610) of FIG. 6) based on an input (1310) for an executable object (1305). For example, at least one processor (300) may perform operations 400 to 440 of FIG. 4 within the mode for acquiring the third image (610).

[0170] Figure 14 shows an example of obtaining an image with improved image quality.

[0171] Referring to FIG. 14, the electronic device (100) may further include a third camera. For example, the third camera may have a third angle of view. For example, the third angle of view may be narrower than the first angle of view (e.g., the first angle of view (125) of FIG. 1). For example, the third camera may be positioned on one side of the electronic device (100) where the first camera (105) is positioned, facing the direction in which the first camera (105) faces. For example, the third camera may include a telephoto lens. For example, the third camera may be described as a telephoto camera.

[0172] For example, at least one processor (300) may acquire a fourth image (1400) using a third camera. For example, the fourth image (1400) may have a higher resolution than the first image (e.g., the first image (500) of FIG. 5A) because the third camera includes a telephoto lens. For example, since the third camera has a third angle of view that is narrower than the first angle of view (125), the scene represented by the fourth image (1400) may be included in the scene represented by the first image (e.g., the first image (500) of FIG. 5A). For example, the first image (500) may include an area (1405) corresponding to the fourth image (1400). For example, at least one processor (300) may improve the image quality of the area (1405) within the first image (500) using the fourth image (1400). For example, at least one processor (300) can use the fourth image (1400) to improve the image quality of the third image (610) in which the deteriorated characteristics of the first image (500) of FIG. 7 are compensated for.

[0173] For example, at least one processor (300) may provide (or input) the first image (500) (or the third image (610)) and the fourth image (1400) to a trained model to improve the image quality. For example, the model may correspond to the model (600) of FIG. 6. For example, the model may include a machine learning model, a deep learning model, and / or a generative artificial intelligence model.

[0174] For example, at least one processor (300) can execute the model by providing (or inputting) the first image (500) (or the third image (610)) and the fourth image (1400) to the model. For example, at least one processor (300) can obtain a fifth image having improved image quality of an area (1405) within the first image (500) (or the third image (610)) based on the execution of the model.

[0175] FIG. 15 is a block diagram of an electronic device within a network environment according to various embodiments.

[0176] Referring to FIG. 15, in a network environment (1500), an electronic device (1501) may communicate with an electronic device (1502) via a first network (1598) (e.g., a short-range wireless communication network), or may communicate with at least one of an electronic device (1504) or a server (1508) via a second network (1599) (e.g., a long-range wireless communication network). In one embodiment, the electronic device (1501) may communicate with the electronic device (1504) via the server (1508). According to one embodiment, the electronic device (1501) may include a processor (1520), a memory (1530), an input module (1550), an audio output module (1555), a display module (1560), an audio module (1570), a sensor module (1576), an interface (1577), a connection terminal (1578), a haptic module (1579), a camera module (1580), a power management module (1588), a battery (1589), a communication module (1590), a subscriber identification module (1596), or an antenna module (1597). In some embodiments, the electronic device (1501) may omit at least one of these components (e.g., the connection terminal (1578)), or may have one or more other components added. In some embodiments, some of these components (e.g., sensor module (1576), camera module (1580), or antenna module (1597)) may be integrated into a single component (e.g., display module (1560)).

[0177] The processor (1520) may, for example, execute software (e.g., a program (1540)) to control at least one other component (e.g., a hardware or software component) of the electronic device (1501) connected to the processor (1520) and perform various data processing or operations. According to one embodiment, as at least a part of the data processing or operations, the processor (1520) may store commands or data received from other components (e.g., a sensor module (1576) or a communication module (1590)) in a volatile memory (1532), process the commands or data stored in the volatile memory (1532), and store result data in a non-volatile memory (1534). According to one embodiment, the processor (1520) may include a main processor (1521) (e.g., a central processing unit or an application processor) or an auxiliary processor (1523) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that can operate independently or together with the main processor (1521). For example, when the electronic device (1501) includes the main processor (1521) and the auxiliary processor (1523), the auxiliary processor (1523) may be configured to use less power than the main processor (1521) or to be specialized for a given function. The auxiliary processor (1523) may be implemented separately from the main processor (1521) or as a part thereof.

[0178] The auxiliary processor (1523) may control at least a portion of functions or states associated with at least one component (e.g., the display module (1560), the sensor module (1576), or the communication module (1590)) of the electronic device (1501), for example, on behalf of the main processor (1521) while the main processor (1521) is in an inactive (e.g., sleep) state, or together with the main processor (1521) while the main processor (1521) is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (1523) (e.g., an image signal processor or a communication processor) may be implemented as a part of another functionally related component (e.g., a camera module (1580) or a communication module (1590)). In one embodiment, the auxiliary processor (1523) (e.g., a neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. The artificial intelligence models may be generated through machine learning. This learning can be performed, for example, on the electronic device (1501) where the artificial intelligence model is executed, or can be performed through a separate server (e.g., server (1508)). The learning algorithm can include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model can include multiple artificial neural network layers.The artificial neural network may be one of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to, or alternatively to, a hardware structure, an artificial intelligence model may include a software structure.

[0179] The memory (1530) can store various data used by at least one component (e.g., the processor (1520) or the sensor module (1576)) of the electronic device (1501). The data can include, for example, software (e.g., the program (1540)) and input data or output data for commands related thereto. The memory (1530) can include volatile memory (1532) or non-volatile memory (1534).

[0180] The program (1540) may be stored as software in memory (1530) and may include, for example, an operating system (1542), middleware (1544), or an application (1546).

[0181] The input module (1550) can receive commands or data to be used in a component of the electronic device (1501) (e.g., a processor (1520)) from an external source (e.g., a user) of the electronic device (1501). The input module (1550) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0182] The audio output module (1555) can output audio signals to the outside of the electronic device (1501). The audio output module (1555) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. In one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0183] The display module (1560) can visually provide information to an external party (e.g., a user) of the electronic device (1501). The display module (1560) may include, for example, a display, a holographic device, or a projector, and a control circuit for controlling the device. In one embodiment, the display module (1560) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch.

[0184] The audio module (1570) can convert sound into an electrical signal, or vice versa. According to one embodiment, the audio module (1570) can acquire sound through the input module (1550), output sound through the sound output module (1555), or an external electronic device (e.g., electronic device (1502)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (1501).

[0185] The sensor module (1576) can detect the operating status (e.g., power or temperature) of the electronic device (1501) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (1576) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0186] The interface (1577) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (1501) with an external electronic device (e.g., the electronic device (1502)). In one embodiment, the interface (1577) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0187] The connection terminal (1578) may include a connector through which the electronic device (1501) may be physically connected to an external electronic device (e.g., the electronic device (1502)). In one embodiment, the connection terminal (1578) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0188] The haptic module (1579) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. In one embodiment, the haptic module (1579) may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0189] The camera module (1580) can capture still images and videos. In one embodiment, the camera module (1580) may include one or more lenses, image sensors, image signal processors, or flashes.

[0190] The power management module (1588) can manage the power supplied to the electronic device (1501). According to one embodiment, the power management module (1588) can be implemented as at least a part of, for example, a power management integrated circuit (PMIC).

[0191] A battery (1589) may power at least one component of the electronic device (1501). In one embodiment, the battery (1589) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0192] The communication module (1590) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (1501) and an external electronic device (e.g., electronic device (1502), electronic device (1504), or server (1508)), and the performance of communication through the established communication channel. The communication module (1590) may operate independently from the processor (1520) (e.g., application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (1590) may include a wireless communication module (1592) (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (1594) (e.g., a local area network (LAN) communication module, or a power line communication module). Any of these communication modules may communicate with an external electronic device (1504) via a first network (1598) (e.g., a short-range communication network such as Bluetooth, wireless-fidelity (Wi-Fi) direct, or infrared data association (IrDA)) or a second network (1599) (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a local area network or a wide area network)). These various types of communication modules may be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (1592) may use subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (1596) to verify or authenticate the electronic device (1501) within a communication network such as the first network (1598) or the second network (1599).

[0193] The wireless communication module (1592) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimizing terminal power and connecting multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency communications (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (1592) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (1592) can support various technologies for securing performance in high-frequency bands, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module (1592) can support various requirements specified in the electronic device (1501), an external electronic device (e.g., the electronic device (1504)), or a network system (e.g., the second network (1599)). According to one embodiment, the wireless communication module (1592) may support a peak data rate (e.g., 20 Gbps or more) for eMBB implementation, a loss coverage (e.g., 164 dB or less) for mMTC implementation, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 1 ms or less for round trip) for URLLC implementation.

[0194] The antenna module (1597) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). In one embodiment, the antenna module (1597) may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). In one embodiment, the antenna module (1597) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as the first network (1598) or the second network (1599), may be selected from the plurality of antennas by, for example, the communication module (1590). A signal or power may be transmitted or received between the communication module (1590) and an external electronic device via the at least one selected antenna. In some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (1597).

[0195] According to various embodiments, the antenna module (1597) may form a mmWave antenna module. In one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent a second side (e.g., a top side or a side side) of the printed circuit board and capable of transmitting or receiving signals in the designated high frequency band.

[0196] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0197] According to one embodiment, commands or data may be transmitted or received between the electronic device (1501) and an external electronic device (1504) via a server (1508) connected to a second network (1599). Each of the external electronic devices (1502 or 1504) may be the same or a different type of device as the electronic device (1501). According to one embodiment, all or part of the operations executed in the electronic device (1501) may be executed in one or more of the external electronic devices (1502, 1504, or 1508). For example, when the electronic device (1501) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (1501) may, instead of or in addition to executing the function or service itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (1501). The electronic device (1501) may process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (1501) may provide an ultra-low latency service by using distributed computing or mobile edge computing, for example. In another embodiment, the external electronic device (1504) may include an Internet of Things (IoT) device. The server (1508) may be an intelligent server utilizing machine learning and / or a neural network.According to one embodiment, an external electronic device (1504) or server (1508) may be included within the second network (1599). The electronic device (1501) may be applied to intelligent services (e.g., smart homes, smart cities, smart cars, or healthcare) based on 5G communication technology and IoT-related technology.

[0198] Electronic devices according to the various embodiments disclosed in this document may take various forms. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances. Electronic devices according to the embodiments of this document are not limited to the aforementioned devices.

[0199] The various embodiments and terminology used in this document are not intended to limit the technical features described in this document to specific embodiments, but should be understood to encompass various modifications, equivalents, or alternatives of the embodiments. In connection with the description of the drawings, similar reference numerals may be used to refer to similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise. In this document, each of the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can include any one of the items listed together in that phrase, or all possible combinations thereof. Terms such as "first", "second", or "first" or "second" may be used merely to distinguish the corresponding component from other corresponding components and do not limit the corresponding components in any other respect (e.g., importance or order). When a (e.g., a first) component is referred to as "coupled" or "connected" to another (e.g., a second) component, with or without the terms "functionally" or "communicatively," it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0200] The term "module" used in various embodiments of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0201] Various embodiments of the present document may be implemented as software (e.g., a program (1540)) including one or more instructions stored in a storage medium (e.g., an internal memory (1536) or an external memory (1538)) readable by a machine (e.g., an electronic device (1501)). For example, a processor (e.g., a processor (1520)) of the machine (e.g., an electronic device (1501)) may call at least one instruction among the one or more instructions stored from the storage medium and execute it. This enables the machine to operate to perform at least one function according to the at least one called instruction. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' simply means that the storage medium is a tangible device and does not contain signals (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently or temporarily on the storage medium.

[0202] According to one embodiment, the method according to various embodiments disclosed in this document may be provided as included in a computer program product. The computer program product may be traded as a commodity between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) via an application store (e.g., Play Store™) or directly between two user devices (e.g., smart phones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0203] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0204] FIG. 16 is a block diagram illustrating a camera module according to various embodiments.

[0205] Referring to FIG. 16, the camera module (1580) may include a lens assembly (1610), a flash (1620), an image sensor (1630), an image stabilizer (1640), a memory (1650) (e.g., a buffer memory), or an image signal processor (1660). The lens assembly (1610) may collect light emitted from a subject that is a target of image capturing. The lens assembly (1610) may include one or more lenses. According to one embodiment, the camera module (1580) may include a plurality of lens assemblies (1610). In this case, the camera module (1580) may form, for example, a dual camera, a 360-degree camera, or a spherical camera. Some of the plurality of lens assemblies (1610) may have the same lens properties (e.g., angle of view, focal length, autofocus, f-number, or optical zoom), or at least one lens assembly may have one or more lens properties that differ from the lens properties of the other lens assemblies. A lens assembly (1610) may include, for example, a wide-angle lens or a telephoto lens.

[0206] The flash (1620) can emit light used to enhance light emitted or reflected from a subject. According to one embodiment, the flash (1620) can include one or more light-emitting diodes (e.g., red-green-blue (RGB) LED, white LED, infrared LED, or ultraviolet LED) or a xenon lamp. The image sensor (1630) can acquire an image corresponding to the subject by converting light emitted or reflected from the subject and transmitted through the lens assembly (1610) into an electrical signal. According to one embodiment, the image sensor (1630) can include one image sensor selected from among image sensors having different properties, such as an RGB sensor, a black and white (BW) sensor, an IR sensor, or a UV sensor, a plurality of image sensors having the same property, or a plurality of image sensors having different properties. Each image sensor included in the image sensor (1630) may be implemented using, for example, a CCD (charged coupled device) sensor or a CMOS (complementary metal oxide semiconductor) sensor.

[0207] The image stabilizer (1640) can move at least one lens or image sensor (1630) included in the lens assembly (1610) in a specific direction or control the operating characteristics of the image sensor (1630) (e.g., adjusting the read-out timing, etc.) in response to movement of the camera module (1580) or the electronic device (1501) including the same. This allows compensating for at least some of the negative effects of the movement on the captured image. In one embodiment, the image stabilizer (1640) can detect such movement of the camera module (1580) or the electronic device (1501) by using a gyro sensor (not shown) or an acceleration sensor (not shown) disposed inside or outside the camera module (1580). According to one embodiment, the image stabilizer (1640) may be implemented as, for example, an optical image stabilizer. The memory (1650) may temporarily store at least a portion of an image acquired through the image sensor (1630) for the next image processing task. For example, when image acquisition is delayed due to a shutter or a plurality of images are acquired at high speed, the acquired original image (e.g., a Bayer-patterned image or a high-resolution image) may be stored in the memory (1650), and a corresponding copy image (e.g., a low-resolution image) may be previewed through the display module (1560). Thereafter, when a specified condition is satisfied (e.g., a user input or a system command), at least a portion of the original image stored in the memory (1650) may be acquired and processed by, for example, the image signal processor (1660). According to one embodiment, the memory (1650) may be configured as at least a portion of the memory (1530), or as a separate memory that operates independently therefrom.

[0208] The image signal processor (1660) can perform one or more image processing operations on an image acquired through an image sensor (1630) or an image stored in a memory (1650). The one or more image processing operations may include, for example, depth map generation, 3D modeling, panorama generation, feature extraction, image synthesis, or image compensation (e.g., noise reduction, resolution adjustment, brightness adjustment, blurring, sharpening, or softening). Additionally or alternatively, the image signal processor (1660) may perform control (e.g., exposure time control, read-out timing control, etc.) on at least one of the components included in the camera module (1580) (e.g., image sensor (1630)). An image processed by the image signal processor (1660) may be stored back in the memory (1650) for further processing or provided to an external component of the camera module (1580) (e.g., memory (1530), display module (1560), electronic device (1502), electronic device (1504), or server (1508). In one embodiment, the image signal processor (1660) may be configured to: The processor (1660) may be configured as at least a part of the processor (1520) or may be configured as a separate processor that operates independently of the processor (1520). When the image signal processor (1660) is configured as a separate processor from the processor (1520), at least one image processed by the image signal processor (1660) may be displayed through the display module (1560) as is or after undergoing additional image processing by the processor (1520).

[0209] According to one embodiment, the electronic device (1501) may include a plurality of camera modules (1580), each having different properties or functions. In this case, for example, at least one of the plurality of camera modules (1580) may be a wide-angle camera, and at least another may be a telephoto camera. Similarly, at least one of the plurality of camera modules (1580) may be a front-facing camera, and at least another may be a rear-facing camera.

[0210] Figure 17 is a schematic diagram of an exemplary AI system.

[0211] Referring to FIG. 17, the AI ​​system (1700) may include an input / output interface (1710), an AI (artificial intelligence) framework (1720), a generative AI model (1730), an application / service component (1780), and / or a knowledge repository (1790).

[0212] The input / output interface (1710) can receive input. The input can include user input and / or data acquired or generated by an electronic device (e.g., the electronic device (100) or the electronic device (1501) described above). The data can include images, videos, and / or sensor data generated by at least one processor (e.g., at least one processor (300) or processor (1520)) of the electronic device (e.g., illuminance data around the electronic device acquired from a sensor or sensor hub (e.g., a coprocessor (1523), posture data (or orientation data) of the electronic device, temperature inside the electronic device (e.g., temperature of the display (320) or temperature of the at least one processor (300)), size information of a display area of ​​the display (320), and / or images acquired through an image sensor (e.g., included in a camera module (1580)) of the electronic device). The user input may include natural language, touch data obtained via touch circuitry included within the display panel (e.g., used to identify input from a finger and / or a stylus), images displayed (and / or to be displayed) on the display panel, and / or video. As a non-limiting example, the user input may be received by the input / output interface (1710) together with context information. The context information may be described as additional information obtained in relation to the user input. The context information may relate to a state when the user input is received (e.g., including a state of the electronic device and / or a state surrounding the electronic device (e.g., a user state)). For example, the context information may include information about one or more software applications running within the electronic device when the user input is received.For example, the contextual information may include information about the location of the electronic device (or the location of the user of the electronic device) at the time the user input is received. For example, the user input may be integrated with the contextual information. For example, the user input integrated with the contextual information may be received by the input / output interface (1710).

[0213] The input / output interface (1710) can transmit (or provide) output. The output may include a result (or result information) generated or acquired by the AI ​​system (1700) based at least in part on the input. The format of the output may vary. For example, the output may include natural language. For example, the output may include content (e.g., including media content and / or multimedia content). For example, the output may include an action related to a user of the electronic device. For example, the output may have a format according to a user setting of the electronic device.

[0214] The input / output interface (1710) can be described as a user query / response interface.

[0215] The AI ​​framework (1720) can be used to obtain information (or data) about the input from the input / output interface (1710) and control one or more components related to the AI ​​system (1700) using the obtained information.

[0216] For example, the prompt design component (1721) within the AI ​​framework (1720) can use the acquired information to generate or obtain a prompt for a generative AI model (1730) (e.g., including a large language model (LLM) or a large multimodal model (LMM)). For example, the prompt design component (1721) can be described as an AI component that uses a learning algorithm and / or a neural network to provide enhanced prompts over time. For example, the prompt design component (1721) can use the acquired information to access a knowledge component (e.g., a knowledge repository (1790)) that includes user preference data, a prompt library, and / or prompt examples to generate or obtain a prompt. The generated prompt can be provided to the generative AI model (1730) (e.g., including an LLM or LMM).

[0217] For example, the API / plugin management component (1722) within the AI ​​framework (1720) may be utilized to facilitate communication for additional information requested (or induced) in connection with the prompt provided (or to be provided) to the generative AI model (1730). For example, the API / plugin management component (1722) may be utilized to create or establish channels for communication with various data sources (e.g., knowledge repositories (1790)). For example, the API / plugin management component (1722) may facilitate access to at least some of the data sources. For example, the API / plugin management component (1722) may be utilized to request another component (e.g., an application / service component (1780)) to perform feedback (or response) in response to the prompt. As a non-limiting example, information obtained (or generated) through the API / plugin management component (1722) may be provided to the prompt design component (1721) for the purpose of generating a prompt. As a non-limiting example, information obtained (or generated) through the API / plugin management component (1722) may be provided to the generative AI model (1730).

[0218] For example, the improvement component (1723) within the AI ​​framework (1720) can at least partially tune (or adjust) (or change) the result (e.g., content) obtained (or output) from the generative AI model (1730). For example, the improvement component (1723) can determine or verify whether the content obtained from the generative AI model (1730) is relevant to the input. For example, the improvement component (1723) can determine or verify whether the content obtained from the generative AI model (1730) contains biased content. For example, the improvement component (1723) can determine or verify whether the content obtained from the generative AI model (1730) contains harmful content. For example, the improvement component (1723) can support or assist in performing additional processing to improve the content obtained from the generative AI model (1730). For example, the improvement component (1723) may support providing hints to the user to improve the content.

[0219] A generative AI model (1730) can be described as an artificial intelligence neural network that generates feedback in response to a prompt. For example, the feedback may include additional data and / or information related to the prompt, but relative to the prompt. For example, the feedback may include new content related to the prompt. For example, the generative AI model (1730) may include a model that generates images and / or a model that generates language. For example, the model that generates images may include a generative adversarial network (GAN) and / or a variational autoencoder (VAE). For example, the model that generates images may include a diffusion-based generative model (e.g., a transformer VAE). For example, the model that generates language may include CHAT-GPT 3 and / or CHAT-GPT 4. For example, a generative AI model (1730) may include an LMM that generates the feedback by recognizing text, images, and / or speech.

[0220] As a non-limiting example, the AI ​​framework (1720) and / or the generative AI model (1730) may be included within an AI module (e.g., including a processing circuit) within the electronic device. For example, the AI ​​module may be operatively coupled with at least one processor of the electronic device (e.g., at least one processor (300) or processor (1520)). For example, the AI ​​module may be operatively coupled with a display driving circuit of the electronic device. For example, the AI ​​module may be operatively coupled with a sensor hub of the electronic device for one or more sensors within the electronic device.

[0221] The technical problems to be achieved in the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by a person having ordinary knowledge in the technical field to which the present disclosure pertains.

[0222] As described above, the electronic device (e.g., the electronic device (100) of FIG. 3) may include at least one processor (e.g., at least one processor (300) of FIG. 3) including a memory (e.g., the memory (310) of FIG. 3) that stores instructions and includes one or more storage media) and a processing circuit. The electronic device may include a first camera (e.g., the first camera (105) of FIG. 3) having a first field of view (FOV) (e.g., the first FOV (125) of FIG. 1), a second camera (e.g., the second camera (110) of FIG. 3) having a second angle of view (FOV) wider than the first angle of view (e.g., the second FOV (130) of FIG. 1), and a display (e.g., the display (320) of FIG. 3). The second camera and the first camera may be disposed on the same side of the electronic device facing the same direction. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to receive an input for acquiring an image through the first camera. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to control the first camera to acquire a first image (e.g., the first image (500) of FIG. 5A) based on the input. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to control the second camera to acquire a second image (e.g., the second image (520) of FIG. 5A) based on the input. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to acquire a third image by compensating for a peripheral area of ​​the acquired first image using the acquired second image.The above instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to store the third image (e.g., the third image (610) of FIG. 6) in the memory.

[0223] For example, the instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to identify a partial image of the second image, corresponding to the peripheral area of ​​the first image, based on acquiring the second image. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to perform an operation of compensating for a deteriorated characteristic of the first image using the partial images of the first image and the second image. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to obtain the third image based on the performed operation. For example, the instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to perform the operation of compensating for the deteriorated characteristic of the first image by providing the partial images of the first image and the second image to a model trained to compensate for the deteriorated characteristic of the image. The above instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to obtain the third image from the trained model.

[0224] For example, the instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to control the first camera to acquire a fourth image having a second resolution less than the first resolution of the first image prior to receiving the input. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to control the second camera to acquire a fifth image having a fourth resolution less than the third resolution of the second image prior to receiving the input. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to identify a partial image of the fifth image corresponding to a peripheral area of ​​the third image based on acquiring the fifth image. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to perform an operation for compensating for a deteriorated characteristic of the fourth image using a partial image of the fourth image and the fifth image. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to display, on the display, a sixth image, as a preview image, obtained based on the performance of the operation for compensating for the deteriorated characteristic of the fourth image.

[0225] For example, the instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to control the first camera to acquire a first set of images including the first image based on the input received in a continuous mode. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to perform an operation for compensating for a degraded characteristic of images in the first set of images using the first set of images and a partial image of the second image. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to store in the memory a second set of images, the second set of images being acquired based on the performance of the operation for compensating for a degraded characteristic of the images in the first set of images, the second set of images including other images corresponding to each of the images in the first set of images.

[0226] For example, the instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to store a file in the memory, the file comprising the second set of images.

[0227] For example, the instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to control the first camera to obtain a first set of images including the first image, based on the input received in a video mode. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to control the second camera to obtain a second set of images including the second image, based on the input received in a video mode. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to identify partial images of a plurality of images within the second set of images, including the partial image. The partial images may correspond to peripheral regions of other images within the first set of images. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to perform an operation for compensating for degraded characteristics of the other images within the first set of images using the first set of images and the partial images. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to store, in the memory, a video comprising a third set of images, the video being obtained based on the performance of the operation for compensating for degraded characteristics of the other images within the first set of images.

[0228] For example, the deteriorated characteristics of the first image may include deteriorated characteristics related to relative illumination, deteriorated characteristics related to tonal range, and / or characteristics deteriorated by barrel distortion.

[0229] For example, the instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to display on the display an executable object for switching modes within a normal mode. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to switch from the normal mode to a mode for acquiring the third image based on an input to the executable object. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to receive the input within the mode for acquiring the third image.

[0230] The method described above can be performed in an electronic device including a memory storing instructions and one or more storage media, a first camera having a first field of view (FOV), a second camera having a second field of view wider than the first FOV, and a display. The second camera (110) and the first camera (105) can be arranged on the same side of the electronic device facing the same direction. The method can include an operation of receiving an input for acquiring an image through the first camera. The method can include an operation of controlling the first camera to acquire a first image based on the input. The method can include an operation of controlling the second camera to acquire a second image based on the input. The method can include an operation of acquiring a third image by compensating for a peripheral area of ​​the acquired first image using the acquired second image. The method can include an operation of storing the third image in the memory.

[0231] For example, the method may include an operation of identifying a partial image of the second image, corresponding to the peripheral area of ​​the first image, based on acquiring the second image. The method may include an operation of performing an operation of compensating for a deteriorated characteristic of the first image using the partial images of the first image and the second image. The method may include an operation of acquiring the third image based on the performed operation. For example, the method may include an operation of performing the operation of compensating for the deteriorated characteristic of the first image by providing the partial images of the first image and the second image to a model trained to compensate for the deteriorated characteristic of the image. The method may include an operation of acquiring the third image from the trained model. For example, the method may include an operation of controlling the first camera to acquire a fourth image having a second resolution that is smaller than the first resolution of the first image before receiving the input. The method may include an operation of controlling the second camera to acquire a fifth image having a fourth resolution smaller than a third resolution of the second image before receiving the input. The method may include an operation of identifying a partial image of the fifth image corresponding to a peripheral area of ​​the third image based on acquiring the fifth image. The method may include an operation of performing an operation of compensating for a deteriorated characteristic of the fourth image using the fourth image and the partial image of the fifth image. The method may include an operation of displaying a sixth image, acquired based on performing the operation of compensating for the deteriorated characteristic of the fourth image, as a preview image on the display.

[0232] For example, the method may include controlling the first camera to acquire a first set of images including the first image based on the input received within a continuous shooting mode. The method may include performing an operation for compensating for degraded characteristics of images in the first set of images using the first set of images and a partial image of the second image. The method may include storing, in the memory, a second set of images, the second set of images being acquired based on performing the operation for compensating for degraded characteristics of the images in the first set of images, the second set of images including other images corresponding to each of the images in the first set of images.

[0233] For example, the method may include storing a file, comprising the second set of images, within the memory.

[0234] For example, the method may include controlling the first camera to obtain a first set of images including the first image based on the input received in the video mode. The method may include controlling the second camera to obtain a second set of images including the second image based on the input received in the video mode. The method may include identifying partial images of each of the images in the second set of images, the partial images including the partial images. The partial images may correspond to peripheral regions of other images in the first set. The method may include performing an operation for compensating for degraded characteristics of the other images in the first set of images using the first set of images and the partial images. The method may include storing a video in the memory, the video including a third set of images, the video being obtained based on performing the operation for compensating for degraded characteristics of the other images in the first set of images.

[0235] For example, the deteriorated characteristics of the first image may include deteriorated characteristics related to relative illumination, deteriorated characteristics related to tonal range, and / or characteristics deteriorated by barrel distortion.

[0236] For example, the method may include an operation of displaying an executable object on the display for switching modes within a normal mode. The method may include an operation of switching from the normal mode to a mode for acquiring the third image based on an input to the executable object. The method may include an operation of receiving the input within the mode for acquiring the third image.

[0237] As described above, the non-transitory computer-readable storage medium may store one or more programs. The one or more programs may include instructions that, when executed by an electronic device including a memory storing instructions and one or more storage media, a first camera having a first field of view (FOV), a second camera having a second field of view wider than the first FOV, and a display, cause the electronic device to receive an input for acquiring an image through the first camera, wherein the second camera (110) and the first camera (105) are positioned on the same side of the electronic device and face the same direction. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to control the first camera to acquire a first image based on the input. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to control the second camera to acquire a second image based on the input. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to acquire a third image by compensating for a peripheral area of ​​the acquired first image using the acquired second image. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to store the third image in the memory.

[0238] For example, the one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to identify a partial image of the second image corresponding to the peripheral area of ​​the first image based on acquiring the second image. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to perform an operation that compensates for a deteriorated characteristic of the first image using the partial images of the first image and the second image. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to acquire the third image based on the performed operation.

[0239] For example, the one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to perform the operation of compensating for the deteriorated characteristic of the first image by providing the partial images of the first image and the second image to a model trained to compensate for the deteriorated characteristic of the image. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to obtain the third image from the trained model. For example, the one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to control the first camera to obtain a fourth image having a second resolution that is smaller than the first resolution of the first image before receiving the input. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to control the second camera to acquire a fifth image having a fourth resolution smaller than the third resolution of the second image prior to receiving the input. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to identify, based on acquiring the fifth image, a partial image of the fifth image corresponding to a peripheral area of ​​the third image. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to perform an operation of compensating for a deteriorated characteristic of the fourth image using the fourth image and the partial image of the fifth image.The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to display, as a preview image, a sixth image obtained based on the performance of the operation of compensating for the deteriorated characteristic of the fourth image on the display.

[0240] For example, the one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to control the first camera to obtain a first set of images including the first image based on the input received in a continuous mode. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to perform an operation for compensating for a degraded characteristic of images in the first set of images using the first set of images and a partial image of the second image. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to store in the memory a second set of images, the second set of images being obtained based on the performance of the operation for compensating for a degraded characteristic of the images in the first set of images, the second set of images including other images corresponding to each of the images in the first set of images.

[0241] For example, the one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to store a file in the memory, the file including the second set of images.

[0242] For example, the one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to control the first camera to obtain a first set of images including the first image, based on the input received in a video mode. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to control the second camera to obtain a second set of images including the second image, based on the input received in a video mode. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to identify partial images of each of the images in the second set of images, including the partial image. The partial images may correspond to peripheral regions of other images in the first set of images. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to perform an operation that compensates for deteriorated characteristics of the other images within the first set of images using the first set of images and the partial images. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to store, in the memory, a video comprising a third set of images, the video being obtained based on the performance of the operation that compensates for deteriorated characteristics of the other images within the first set of images.

[0243] For example, the deteriorated characteristics of the first image may include deteriorated characteristics related to relative illumination, deteriorated characteristics related to tonal range, and / or characteristics deteriorated by barrel distortion.

[0244] For example, the one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to display on the display an executable object for switching modes within a normal mode. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to switch from the normal mode to a mode for acquiring the third image based on an input to the executable object. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to receive the input within the mode for acquiring the third image.

[0245] It will be apparent that the various embodiments of the present disclosure according to the claims and the description of the specification may be realized in the form of hardware, software, or a combination of hardware and software.

[0246] Such software may be stored on a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium stores one or more computer programs (software modules), and the one or more computer programs include computer-executable instructions that, when executed by one or more processors of the electronic device, cause the electronic device to perform the method of the present disclosure.

[0247] Such software may be stored in a volatile or non-volatile storage form, for example, erasable, or rewritable, or non-rewritable, or in a storage device such as a read only memory (ROM) in the form of memory, for example, random access memory (RAM), memory chips, devices, or integrated circuits, or in an optically or magnetically readable medium, for example, a compact disk (CD), a digital versatile disc (DVD), a magnetic disk, a magnetic tape, or the like. It will be apparent that the storage devices and storage media are various embodiments of non-transitory machine-readable storage suitable for storing computer programs or computer programs comprising instructions that, when executed, implement various embodiments of the present disclosure. Accordingly, various embodiments provide code for implementing an apparatus or method as claimed in any of the claims of this specification and a non-transitory machine-readable storage device storing such programs.

[0248] The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned will be clearly understood by a person having ordinary skill in the art to which the present disclosure pertains.

Claims

1. In an electronic device (100), A memory (310) storing instructions and including one or more storage media; A first camera (105) having a first field of view (FOV) (125); A second camera (110) having a second angle of view (130) wider than the first angle of view (125); the second camera (110) and the first camera (105) are arranged on the same side of the electronic device and facing the same direction. display (320); and At least one processor (300) comprising a processing circuit, The above instructions, when individually or collectively executed by the at least one processor (300), Receive input for acquiring an image through the first camera, Based on the above input: To obtain the first image (500), control the first camera (105); and To obtain the second image (520), the second camera (110) is controlled, By using the acquired second image (520), a third image (610) is acquired by compensating the surrounding area (510-1, 510-2) of the acquired first image (500); and To store the third image (610) in the memory (310), causing the above electronic device (100), Electronic device (100).

2. In claim 1, The above instructions, when individually or collectively executed by the at least one processor (300), Based on acquiring the second image (520), a partial image (530-1, 530-2) of the second image (520) corresponding to the peripheral area (510-1, 510-2) of the first image (500) is identified, An operation of compensating for deteriorated characteristics of the first image (500) is performed using the partial images (530-1, 530-2) of the first image (500) and the second image (520), and To obtain the third image (610) based on the above-described performed operation, causing the above electronic device (100), Electronic device (100).

3. In claim 2, The above instructions, when individually or collectively executed by the at least one processor (300), By providing the partial images (530-1, 530-2) of the first image (500) and the second image (520) to a model trained to compensate for the deteriorated characteristics of the images, the operation of compensating for the deteriorated characteristics of the first image (500) is performed, and To obtain the third image (610) from the trained model, causing the above electronic device (100), Electronic device (100).

4. In claim 1, The above instructions, when individually or collectively executed by the at least one processor (300), Before receiving the above input: Controlling the first camera (105) to obtain a fourth image having a second resolution smaller than the first resolution of the first image (500); and To obtain a fifth image having a fourth resolution smaller than the third resolution of the second image (520), the second camera (110) is controlled, and Based on acquiring the fifth image, a partial image of the fifth image corresponding to a peripheral area of ​​the third image (610) is identified; Performing an operation of compensating for deteriorated characteristics of the fourth image by using the partial images of the fourth image and the fifth image; and To display a sixth image, obtained based on the performance of the operation for compensating for the deteriorated characteristics of the fourth image, as a preview image on the display (320). causing the above electronic device (100), Electronic device (100).

5. In claim 2, The above instructions, when individually or collectively executed by the at least one processor (300), Controlling the first camera (105) to obtain a first set of images including the first image (500), based on the input received within a continuous mode; An operation of compensating for deteriorated characteristics of images in the first set of images using the first set of images and the partial images (530-1, 530-2) of the second image (520); and A second set of images, which is obtained based on the performance of the operation of compensating for the deteriorated characteristics of the images in the first set of images, and which includes other images corresponding to each of the images in the first set of images, is stored in the memory (310). causing the above electronic device (100), Electronic device (100).

6. In claim 5, The above instructions, when individually or collectively executed by the at least one processor (300), To store a file (a file) containing the second set of images in the memory (310), causing the above electronic device (100), Electronic device (100).

7. In claim 2, The above instructions, when individually or collectively executed by the at least one processor (300), Based on the above input received within video mode: Controlling the first camera (105) to obtain a first set of images including the first image (500); and To obtain a second set of images including the second image (520), controlling the second camera (110), and Identifying partial images of each of the images in the second set of images, including the partial image, wherein the partial images correspond to surrounding areas of other images in the first set of images; and Using the first set of images and the partial images, an operation is performed to compensate for deteriorated characteristics of the other images within the first set of images; and Store a video including a third set of images, obtained based on the performance of the operation of compensating for the deteriorated characteristics of the other images in the first set of images, in the memory (310). causing the above electronic device (100), Electronic device (100).

8. In claim 2, The deteriorated characteristics of the first image (500) are: Including degraded characteristics related to relative illumination, degraded characteristics related to gradation, and / or degraded characteristics due to barrel distortion. Electronic device (100).

9. In claim 1, The above instructions, when individually or collectively executed by the at least one processor (300), An executable object for switching modes within the normal mode is displayed on the display (320), Based on the input for the executable object, switching from the general mode to a mode for acquiring the third image (610), and To receive the input within the above mode for obtaining the above third image (610), causing the above electronic device (100), Electronic device (100).

10. A method for executing in an electronic device (100) including a memory (310) storing instructions and including one or more storage media, a first camera (105) having a first angle of view (125), a second camera (110) having a second angle of view (130) wider than the first angle of view (125), and a display (320), wherein the second camera (110) and the first camera (105) are arranged on the same side of the electronic device (100) and facing the same direction. An operation of receiving an input for acquiring an image through the first camera (105), Based on the above input: To obtain the first image (500), an operation of controlling the first camera (105); and In order to obtain a second image (520), an operation of controlling the second camera (110), An operation of obtaining a third image (610) by compensating the surrounding area (510-1, 510-2) of the obtained first image (500) using the obtained second image (520), and Including an operation of storing the third image (610) in the memory (310). method.

11. In claim 10, the method comprises: An operation of identifying a partial image (530-1, 530-2) of the second image (520) corresponding to the peripheral area (510-1, 510-2) of the first image (500) based on acquiring the second image (520), An operation of performing an operation of compensating for deteriorated characteristics of the first image (500) by using the partial images (530-1, 530-2) of the first image (500) and the second image (520), and Including an operation of obtaining the third image (610) based on the above-described performed operation. method.

12. In claim 11, the method comprises: An operation of performing the operation of compensating for the deteriorated characteristics of the first image (500) by providing the partial images (530-1, 530-2) of the first image (500) and the second image (520) to a model trained to compensate for the deteriorated characteristics of the images, and Including an operation of obtaining the third image (610) from the trained model. method.

13. In claim 10, the method comprises: Before receiving the above input: An operation of controlling the first camera (105) to obtain a fourth image having a second resolution smaller than the first resolution of the first image (500); and An operation of controlling the second camera (110) to obtain a fifth image having a fourth resolution that is smaller than the third resolution of the second image (520); An operation of identifying a partial image of the fifth image corresponding to a peripheral area of ​​the third image (610) based on acquiring the fifth image; An operation of performing an operation of compensating for deteriorated characteristics of the fourth image by using the partial images of the fourth image and the fifth image; and An operation of displaying a sixth image, obtained based on the performance of the operation for compensating for the deteriorated characteristics of the fourth image, as a preview image on the display (320), method.

14. In claim 11, the method comprises: An operation of controlling the first camera (105) to obtain a first set of images including the first image (500), based on the input received within a continuous mode; An operation of performing an operation of compensating for deteriorated characteristics of images in the first set of images using the first set of images and the partial images (530-1, 530-2) of the second image (520); and An operation of storing a second set of images, which includes other images corresponding to each of the images in the first set of images, in the memory (310), based on the performance of the operation of compensating for the deteriorated characteristics of the images in the first set of images, method.

15. In a non-transitory computer-readable storage medium storing one or more programs, the one or more programs are executed by an electronic device (100) including a memory (310) storing instructions and including one or more storage media, a first camera (105) having a first angle of view (125), and a second camera (110) having a second angle of view (130) wider than the first angle of view (125), and a display (320), wherein the second camera (110) and the first camera (105) are arranged on the same side of the electronic device (100) and facing the same direction. Receive input for acquiring an image through the first camera (105), Based on the above input: To obtain the first image (500), control the first camera (105); and To obtain the second image (520), the second camera (110) is controlled, By using the acquired second image (520), a third image (610) is acquired by compensating the surrounding area (510-1, 510-2) of the acquired first image (500); and To store the third image (610) in the memory (310), Including instructions that cause the above electronic device (100), Non-transitory computer-readable storage medium. In an electronic device (100), A memory (310) storing instructions and including one or more storage media; A first camera (105) having a first field of view (FOV) (125); A second camera (110) having a second angle of view (130) wider than the first angle of view (125); the second camera (110) and the first camera (105) are arranged on the same side of the electronic device and facing the same direction. display (320); and At least one processor (300) comprising a processing circuit, The above instructions, when individually or collectively executed by the at least one processor (300), Receive input for acquiring an image through the first camera, Based on the above input: To obtain the first image (500), control the first camera (105); and To obtain the second image (520), the second camera (110) is controlled, By using the acquired second image (520), a third image (610) is acquired by compensating the surrounding area (510-1, 510-2) of the acquired first image (500); and To store the third image (610) in the memory (310), causing the above electronic device (100), Electronic device (100).

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