Electronic device, non-transitory computer-readable storage medium, and method for performing noise reduction of image

Abstract

An electronic device may comprise at least one processor and a memory for storing instructions. The instructions, when executed individually or collectively by the at least one processor, may cause the electronic device to: acquire first image data; identify second image data on which noise reduction has been performed before performing the noise reduction on the first image data; acquire third image data by blending the first image data and the second image data; identify weight data for spatial noise reduction in the first image data according to the third image data; and perform the noise reduction on the first image data on the basis of performing the spatial noise reduction by applying the weight data to the first image data.

Description

Electronic device for performing noise reduction of an image, non-transient computer-readable storage medium, and method

[0001] The following descriptions relate to an electronic device for performing noise reduction of an image, a non-transient computer-readable storage medium, and a method.

[0002] An electronic device can perform noise reduction of an image to reduce noise present in the image through image processing. For example, the noise reduction may include temporal noise reduction that blends two or more images and spatial noise reduction that corrects the image based on the similarity between pixels within a single image.

[0003] The information described above may be provided as related art for the purpose of aiding understanding of the present disclosure. No claim or determination is made as to whether any of the foregoing may be applied as prior art related to the present disclosure.

[0004] An electronic device is provided. The electronic device may include at least one processor comprising a processing circuit and a memory comprising one or more storage media for storing instructions. The instructions may cause the electronic device to acquire a first image data when executed individually or collectively by the at least one processor. The instructions may cause the electronic device to identify a second image data on which noise reduction has been performed before performing noise reduction on the first image data, based on acquiring the first image data when executed individually or collectively by the at least one processor. The instructions may cause the electronic device to acquire a third image data by blending the first image data and the second image data when executed individually or collectively by the at least one processor. The above instructions may cause the electronic device to identify weight data for spatial noise reduction of the first image data according to the third image data when executed individually or collectively by the at least one processor. The above instructions may cause the electronic device to perform the noise reduction of the first image data based on performing the spatial noise reduction by applying the weight data to the first image data when executed individually or collectively by the at least one processor.

[0005] A non-transient computer-readable storage medium is provided. The non-transient computer-readable storage medium may store one or more programs. The one or more programs may include instructions that cause the electronic device to acquire a first image data when executed by the electronic device. The one or more programs may include instructions that cause the electronic device to identify a second image data on which noise reduction has been performed before performing noise reduction on the first image data, based on acquiring the first image data when executed by the electronic device. The one or more programs may include instructions that cause the electronic device to acquire a third image data by blending the first image data and the second image data when executed by the electronic device. The one or more programs may include instructions that cause the electronic device to identify weight data for spatial noise reduction of the first image data according to the third image data when executed by the electronic device. The above one or more programs may include instructions that cause the electronic device to perform the noise reduction of the first image data based on performing the spatial noise reduction by applying the weight data to the first image data when executed by the electronic device.

[0006] A method is provided. The method may be performed by an electronic device. The method may include an operation of acquiring first image data. Based on acquiring the first image data, the method may include an operation of identifying second image data on which noise reduction has been performed before performing noise reduction on the first image data. The method may include an operation of acquiring third image data by blending the first image data and the second image data. The method may include an operation of identifying weight data for spatial noise reduction of the first image data according to the third image data. The method may include an operation of performing noise reduction on the first image data based on performing spatial noise reduction by applying the weight data to the first image data.

[0007] Figure 1 is a schematic view of an exemplary electronic device.

[0008] Figure 2 illustrates an example of an environment in which noise reduction of an image is performed within an electronic device.

[0009] Figure 3 is a flowchart illustrating a method for performing spatial noise reduction of an image within an electronic device.

[0010] FIG. 4 illustrates an example of an environment for identifying weights for spatial noise reduction of an image performed within an electronic device.

[0011] Figure 5 is a diagram illustrating an image obtained according to noise reduction of an image performed within an electronic device.

[0012] Figure 6 illustrates an example of an environment in which noise reduction of an image is performed within an electronic device.

[0013] Figure 7 illustrates an example of an environment in which noise reduction of an image is performed within an electronic device.

[0014] FIG. 8 is a block diagram of an electronic device in a network environment according to various embodiments.

[0015] Figure 1 is a schematic view of an exemplary electronic device.

[0016] Referring to FIG. 1, the electronic device (101) may include at least one processor (110), a memory (120), and at least one camera (130). The electronic device (101) may include at least a part of the electronic device (801) of FIG. 8 or correspond to at least a part of the electronic device (801) of FIG. 8.

[0017] At least one processor (110) may include a processing circuit. At least one processor (110) may include a single processor or multiple processors. At least one processor (110) may control the memory (120) and / or one or more components (e.g., at least one camera (130)) of the electronic device (101). For example, at least one processor (110) may include at least a part of the processor (820) of FIG. 8 or correspond to at least a part of the processor (820) of FIG. 8. For example, at least one processor (110) may include an image signal processor included in the camera module (880) of FIG. 8.

[0018] Memory (120) may store one or more programs configured to be executed individually and / or collectively by at least one processor (110). The one or more programs may include instructions. The instructions may cause an electronic device (101) to perform operations described with reference to FIGS. 2 through 7. Memory (120) may include one or more storage media. At least some of the one or more programs may be available to manage, control, and / or execute image processing associated with noise reduction of an image, as described below. For example, memory (120) may include at least some of the memory (830) of FIG. 8 or correspond to at least some of the memory (830) of FIG. 8.

[0019] At least one camera (130) can capture (or take) images (e.g., still images) and video. For example, at least one camera (130) may include one or more lenses, image sensors, and / or flashes. For example, at least one camera (130) may include at least a part of the camera module (880) of FIG. 8 or correspond to at least a part of the camera module (880) of FIG. 8.

[0020] The electronic device (101) can sequentially acquire two or more images through at least one camera (130). For example, the two or more images may include a first image and a second image acquired sequentially through at least one camera (130). For example, the second image may be described as a previous image of the first image. For example, the second image may be used to reduce noise present in the first image.

[0021] The electronic device (101) can perform noise reduction to reduce noise present in an image through image processing. For example, the noise reduction may include temporal noise reduction that blends two or more images and spatial noise reduction that corrects the image according to the similarity between pixels within a single image.

[0022] The electronic device (101) can perform the temporal noise reduction by blending the two or more images according to a temporal weight for the temporal noise reduction. The temporal weight can be described as a ratio for blending the two or more images. The temporal weight can be obtained based on performing moving object detection on the two or more images. For example, the electronic device (101) can perform the temporal noise reduction of the first image by blending the first image and the second image according to the temporal weight. For example, the operation of blending the first image and the second image can be performed for the temporal noise reduction of the first image. For example, the temporal noise reduction of the first image may include an operation of correcting (or compensating for) (or determining) the value of the first pixel of the first image using the value of the second pixel of the second image. For example, the location of the first pixel and the location of the second pixel may correspond to each other.

[0023] The electronic device (101) can perform spatial noise reduction by applying a spatial weight for spatial noise reduction to an image. The spatial weight may be obtained based on similarity between pixels within an image. The spatial weight may be obtained based on comparing the value of a pixel in the image with at least one value of at least one pixel in the image adjacent to the pixel in the image. For example, the electronic device (101) can perform spatial noise reduction of the first image by applying the spatial weight to the first image. For example, the operation of applying the spatial weight to the first image may be performed for spatial noise reduction of the first image. For example, the spatial noise reduction of the first image may include an operation of correcting (or compensating for) (or determining) the value of a pixel of the first image using at least one value of at least one pixel of the first image adjacent to the pixel of the first image.

[0024] The electronic device (101) may obtain spatial weights based on a blended image (e.g., a third image) obtained by blending the image with a previous image (e.g., a second image) while noise reduction of the image (e.g., a first image) is being performed. The electronic device (101) may perform spatial noise reduction of the image by applying the obtained spatial weights to the image. For example, the blended image may be described as an image in which the first image and the second image are blended according to the temporal noise reduction of the first image. For example, the electronic device (101) may increase the amount of noise removed through spatial noise reduction by identifying the spatial weights for spatial noise reduction based on the blended image obtained according to the temporal noise reduction. For example, the electronic device (101) can minimize changes in image details as a result of performing noise reduction by performing spatial noise reduction, which applies the spatial weights identified according to the blended image to the first image, which is an unblended image. A method of operation for performing noise reduction of an image within the electronic device (101) is described with reference to FIG. 2.

[0025] Figure 2 illustrates an example of an environment in which noise reduction of an image is performed within an electronic device.

[0026] Referring to FIG. 2, an environment (200) in which noise reduction of an image is performed within an electronic device (101) is illustrated. The environment (200) may include a temporal weight generator (210), an image blender (220), a spatial noise reducer (230), and an image blender (240). The spatial noise reducer (230) may include a spatial weight generator (231) and a spatial noise reduction (SNR) filter (232). Each of the temporal weight generator (210), the image blender (220), the spatial weight generator (231), the SNR filter (232), and the image blender (240) may be described as a program executed by at least one processor (110) in relation to noise reduction of an image. For example, the programs may be stored in memory (120).

[0027] The electronic device (101) can perform noise reduction to reduce noise present in an image through image processing. For example, the noise reduction may include temporal noise reduction that blends two or more images and spatial noise reduction that corrects the image according to the similarity between pixels within a single image.

[0028] In the environment (200), the electronic device (101) can perform noise reduction of the first image data (image1) based on sequentially performing a first temporal noise reduction, a spatial noise reduction, and a second temporal noise reduction on the first image data (image1). For example, the first temporal noise reduction can be performed through a temporal weight generation unit (210) and an image blender (220) in the environment (200). For example, the spatial noise reduction can be performed through a spatial weight generation unit (231) and an SNR filter (232) in the environment (200). For example, the second temporal noise reduction can be performed through an image blender (240) in the environment (200).

[0029] The electronic device (101) can identify two or more images that are sequentially acquired through at least one camera (130). For example, the two or more images may include a first image and a second image. For example, the second image may be described as a previous image of the first image acquired before the first image was acquired through at least one camera (130). For example, the first image may be described as the 'N'th ('N' is a natural number greater than or equal to 2) video frame among the video frames acquired through at least one camera (130). For example, the second image may be described as the 'N-1'th video frame among the video frames acquired through at least one camera (130). For example, the ordinal number of the video frames may be determined according to the order in which they are acquired through the camera (130). For example, the first image and the second image, respectively, can each be described as raw data in which noise reduction has not been performed.

[0030] The electronic device (101) can acquire a first image data (image1). The first image data (image1) may correspond to the first image acquired through at least one camera (130). For example, the first image data (image1) may be described as image data in which the noise reduction is performed within the environment (200).

[0031] The electronic device (101) can identify a second image data (image2) obtained according to the noise reduction performed before the noise reduction of the first image data (image1) is performed, based on acquiring the first image data (image1). The second image data (image2) may correspond to the second image obtained through at least one camera (130) before the first image is acquired. For example, the second image data (image2) may be described as image data in which at least a portion of the noise reduction is performed within the environment (200). For example, the second image data (image2) may be used for the noise reduction of the first image data (image1) within the environment (200).

[0032] For example, since the second image data (image2) is image data in which at least a portion of the noise reduction is performed within the environment (200), the first noise difference between the first image and the first image data (image1) may be smaller than the second noise difference between the second image and the second image data (image2).

[0033] In one embodiment, each of the first image data (image1) and the second image data (image2) may be image data in which at least one noise reduction is performed, distinct from the noise reduction performed within the environment (200). In one embodiment, each of the first image data (image1) and the second image data (image2) may be image data in which at least one noise reduction is not performed, distinct from the noise reduction performed within the environment (200). For example, since the second image data (image2) is image data in which at least a portion of the noise reduction is performed within the environment (200), the number of noise reductions performed on the first image data (image1) may be smaller than the number of noise reductions performed on the second image data (image2).

[0034] The electronic device (101) can identify a first temporal weight data (W_TNR) for the first temporal noise reduction of the first image data (image1) based on comparing the first image data (image1) with the second image data (image2) to perform moving object detection through the temporal weight generation unit (210). The second image data (image2) input through the temporal weight generation unit (210) can be described as image data in which at least a portion of the noise reduction has been performed within the environment (200). For example, the second image data (image2) input through the temporal weight generation unit (210) may be image data in which the first temporal noise reduction, the spatial noise reduction, and the second temporal noise reduction have been performed within the environment (200). As another example, the second image data (image2) input through the temporal weight generation unit (210) may be image data in which the first temporal noise reduction and the spatial noise reduction have been performed within the environment (200). As yet another example, the second image data (image2) input through the temporal weight generation unit (210) may be image data in which the first temporal noise reduction has been performed within the environment (200).

[0035] For example, the electronic device (101) may perform the moving object detection based on identifying the similarity (or difference) between the position of the moving object in the first image data (image1) and the position of the moving object in the second image data (image2) through the temporal weight generation unit (210). For example, the second image data (image2) may be image data in which the position of the moving object in the second image data (image2) is compensated (or warped) according to the position of the moving object in the first image data (image1). However, the present disclosure is not necessarily limited thereto. For example, in order to increase the speed of processing the noise reduction within the environment (200) and reduce the power consumed according to the noise reduction, the electronic device (101) may perform the moving object detection using the second image data (image2) in which the position of the moving object is not compensated (or warped) through the temporal weight generation unit (210). For example, the method for identifying the similarity (or difference) for the above-mentioned moving object detection can be described in various ways depending on the embodiment. For example, the method can be described as PD (pixel difference), SAD (sum of absolute difference), or NCC (normalized cross-correlation).

[0036] For example, the electronic device (101) can identify a first temporal weight data (W_TNR) based on performing the moving object detection through the temporal weight generation unit (210). For example, the first temporal weight data (W_TNR) may represent a ratio (e.g., a:1-a) for blending the first image data (image1) and the second image data (image2). For example, 'a' may be a weight applied to the first image data (image1). For example, '1-a' may be a weight applied to the second image data (image2). For example, 'a' may be a number between 0 and 1. For example, the electronic device (101) may lower 'a' to increase the blending ratio of the second image data (image2) as the similarity between the position of the moving object in the first image data (image1) and the position of the moving object in the second image data (image2) is higher. For example, the electronic device (101) may increase 'a' to increase the blending ratio of the first image data (image1) as the similarity between the position of the moving object in the first image data (image1) and the position of the moving object in the second image data (image2) is lower. For example, the electronic device (101) may mitigate the ghosting phenomenon caused by the second image data (image2) by increasing 'a' as the similarity is lower.

[0037] The electronic device (101) can perform the first temporal noise reduction of the first image data (image1) by blending the first image data (image1) and the second image data (image2) according to the first temporal weight data (W_TNR) through the image blender (220). The electronic device (101) can obtain the third image data (image3) based on performing the first temporal noise reduction of the first image data (image1) through the image blender (220). The second image data (image2) input through the image blender (220) can be described as image data in which at least a portion of the noise reduction has been performed within the environment (200). For example, the second image data (image2) input through the image blender (220) may be image data in which the first temporal noise reduction, the spatial noise reduction, and the second temporal noise reduction are performed within the environment (200). As another example, the second image data (image2) input through the image blender (220) may be image data in which the first temporal noise reduction and the spatial noise reduction are performed within the environment (200). As yet another example, the second image data (image2) input through the image blender (220) may be image data in which the first temporal noise reduction is performed within the environment (200). For example, the second image data (image2) may have the same resolution as the first image data (image1) or a lower resolution than the first image data (image1), depending on the embodiment.For example, if the second image data (image2) has a lower resolution than the first image data (image1), the electronic device (101) can adjust the resolution of the second image data (image2) to be the same as the resolution of the first image data (image1) through the image blender (220).

[0038] For example, the operation of blending the first image data (image1) and the second image data (image2) may be performed with respect to the first temporal noise reduction of the first image data (image1). For example, the first temporal noise reduction of the first image data (image1) may include the operation of correcting (or compensating for) (or determining) the value of the first pixel of the first image data (image1) using the value of the second pixel of the second image data (image2). For example, the location of the first pixel and the location of the second pixel may correspond to each other. For example, the electronic device (101) may obtain the third image data (image3) by blending the first image data (image1) and the second image data (image2) according to the first temporal weight data (W_TNR). For example, the pixel value of the third image data (image3) can be described as the sum of the value of the first pixel of the first image data (image1) multiplied by 'a' and the value of the second pixel of the second image data (image2) multiplied by '1-a'. The third image data (image3) can be described as image data with reduced noise compared to the first image data (image1).

[0039] The electronic device (101) can identify spatial weight data (W_SNR) for spatial noise reduction of the first image data (image1) according to the third image data (image3) through the spatial weight generation unit (231). For example, the electronic device (101) can identify similarity (or difference) between pixels within the third image data (image3) by comparing a pixel of the third image data (image3) with at least one pixel of the third image data (image3) adjacent to said pixel of the third image data (image3) through the spatial weight generation unit (231). For example, the method of identifying said similarity (or difference) between said pixels can be described in various ways according to the embodiment. For example, said method can be described as PD (pixel difference), SAD (sum of absolute difference), or NCC (normalized cross-correlation). For example, the electronic device (101) can obtain spatial weight data (W_SNR) based on the similarity (or difference) between the pixels within the third image data (image3). For example, the spatial weight data (W_SNR) may indicate a higher spatial weight as the similarity increases.

[0040] The electronic device (101) can perform spatial noise reduction of the first image data (image1) by applying spatial weight data (W_SNR) to the first image data (image1) through an SNR filter (232). The electronic device (101) can acquire the fourth image data (image4) based on performing the spatial noise reduction of the first image data (image1) through an SNR filter (232). For example, the operation of applying spatial weight data (W_SNR) acquired according to the third image data (image3) to the first image data (image1) can be performed with respect to the spatial noise reduction of the first image data (image1). For example, the spatial noise reduction of the first image data (image1) may include an operation of correcting (or compensating for) (or determining) the value of the first pixel of the first image data using at least one value of at least one pixel of the third image data adjacent to the second pixel of the third image data. For example, the location of the first pixel and the location of the second pixel may correspond to each other. For example, the electronic device (101) may obtain the fourth image data (image4) by applying spatial weight data (W_SNR) obtained according to the third image data (image3) to the first image data (image1). For example, the value of the pixel of the fourth image data (image4) may be described as the weighted mean of the values ​​of the pixels of the first image data (image1) to which the spatial weight data (W_SNR) is applied. For example, the SNR filter (232) can be described as a bilateral filter using a weighted average or a non-local means (NLM) filter using a weighted average.The fourth image data (image4) can be described as image data with reduced noise compared to the first image data (image1).

[0041] In one embodiment, the electronic device (101) can perform spatial noise reduction of the first image data (image1) using the first temporal weight data (W_TNR) through the SNR filter (232).

[0042] For example, the electronic device (101) can mitigate the decrease in accuracy of calculating spatial weights even if the first image data (image1) corresponds to a low-light image and / or an image having a high noise level by identifying spatial weight data (W_SNR) for spatial noise reduction based on a third image data (image3) in which the first image data (image1) and the second image data (image2) are blended according to the first temporal noise reduction. For example, the electronic device (101) can increase the amount of noise removed through the spatial noise reduction.

[0043] For example, the electronic device (101) can minimize changes in image details as a result of performing noise reduction by applying spatial weight data (W_SNR) identified according to the third image data (image3) to the first image data (image1), which is an unblended image.

[0044] The electronic device (101) can perform the second temporal noise reduction of the first image data (image1) by blending the second image data (image2) and the fourth image data (image4) according to the second temporal weight data (W_TNRa) through the image blender (240). The electronic device (101) can obtain the fifth image data (image5) based on performing the second temporal noise reduction of the first image data (image1) through the image blender (240). The second image data (image2) input through the image blender (240) can be described as image data in which at least a portion of the noise reduction has been performed within the environment (200). For example, the second image data (image2) input through the image blender (240) may be image data in which the first temporal noise reduction, the spatial noise reduction, and the second temporal noise reduction are performed within the environment (200). As another example, the second image data (image2) input through the image blender (240) may be image data in which the first temporal noise reduction and the spatial noise reduction are performed within the environment (200). As yet another example, the second image data (image2) input through the image blender (240) may be image data in which the first temporal noise reduction is performed within the environment (200).

[0045] The second temporal weight data (W_TNRa) can be used to blend the fourth image data (image4) and the second image data (image2). For example, the second temporal weight data (W_TNRa) may represent a ratio (e.g., b:1-b) for blending the fourth image data (image4) and the second image data (image2). For example, 'b' may be a weight applied to the fourth image data (image4). For example, '1-b' may be a weight applied to the second image data (image2). For example, 'b' may be a number between 0 and 1. In one embodiment, the second temporal weight data (W_TNRa) may correspond to the first temporal weight data (W_TNR). For example, 'b' represented by the second temporal weighting data (W_TNRa) may be the same as 'a' represented by the first temporal weighting data (W_TNR). In one embodiment, the second temporal weighting data (W_TNRa) may be different from the first temporal weighting data (W_TNR). For example, 'b' represented by the second temporal weighting data (W_TNRa) may be different from 'a' represented by the first temporal weighting data (W_TNR).

[0046] For example, the operation of blending the fourth image data (image4) and the second image data (image2) may be performed with respect to the second temporal noise reduction of the first image data (image1). For example, the second temporal noise reduction of the first image data (image1) may include an operation of correcting (or compensating for) (or determining) the value of the first pixel of the fourth image data (image4) using the value of the second pixel of the second image data (image2). For example, the location of the first pixel and the location of the second pixel may correspond to each other. For example, the electronic device (101) may obtain the fifth image data (image5) by blending the fourth image data (image4) and the second image data (image2) according to the second temporal weight data (W_TNRa). For example, the pixel value of the fifth image data (image5) can be described as the sum of the value of the first pixel of the fourth image data (image4) multiplied by 'b' and the value of the second pixel of the second image data (image2) multiplied by '1-b'. The fifth image data (image5) can be described as image data with reduced noise compared to the fourth image data (image4). For example, the electronic device (101) can store the fifth image data (image5) as a final image in memory (120) in relation to gallery application software. As another example, the electronic device (101) can perform a different noise reduction on the fifth image data (image5) that is distinct from the noise reduction performed within the environment (200).

[0047] In one embodiment, the electronic device (101) can obtain a fifth image data (image5) by blending the third image data (image3) and the fourth image data (image4) according to the second temporal weighting data (W_TNRa).

[0048] Figure 3 is a flowchart illustrating a method for performing spatial noise reduction of an image within an electronic device.

[0049] Referring to FIG. 3, in operation 301, at least one processor (110) can acquire first image data. For example, the first image data may be described as image data on which noise reduction is to be performed. For example, the first image data may correspond to a first image acquired through at least one camera (130). For example, the first image may be described as the 'N'th ('N' is a natural number greater than or equal to 2) video frame among video frames acquired through at least one camera (130). For example, the ordinal number of the video frames may be determined according to the order in which they are acquired through the camera (130). For example, the first image may be described as raw data on which noise reduction has not been performed.

[0050] In operation 302, at least one processor (110) can identify a second image data to which noise reduction has been performed before performing the noise reduction on the first image data, based on acquiring the first image data. For example, the second image data may be described as image data to which at least a portion of the noise reduction has been performed. For example, the second image data may be used for the noise reduction of the first image data. For example, the second image data may correspond to a second image acquired through at least one camera (130). For example, the second image may be described as the 'N-1'th video frame among the video frames acquired through at least one camera (130). For example, the second image may be described as raw data to which noise reduction has not been performed.

[0051] In operation 303, at least one processor (110) can identify temporal weight data for temporal noise reduction based on the first image data and the second image data. For example, at least one processor (110) can identify temporal weight data for temporal noise reduction of the first image data based on comparing the first image data with the second image data to perform moving object detection.

[0052] For example, at least one processor (110) may perform the moving object detection based on identifying the similarity (or difference) between the position of the moving object in the first image data and the position of the moving object in the second image data. For example, the method of identifying the similarity (or difference) for the moving object detection may be described in various ways according to the embodiment. For example, the method may be described as a pixel difference (PD), a sum of absolute difference (SAD), or a normalized cross-correlation (NCC).

[0053] For example, at least one processor (110) may identify the temporal weight data based on performing the moving object detection. For example, the temporal weight data may represent a blending ratio (e.g., a:1-a) for the first image data and the second image data. For example, 'a' may be a weight applied to the first image data. For example, '1-a' may be a weight applied to the second image data. For example, 'a' may be a number between 0 and 1. For example, at least one processor (110) may lower 'a' to increase the blending ratio of the second image data as the similarity between the position of the moving object in the first image data and the position of the moving object in the second image data increases. For example, at least one processor (110) can increase 'a' to increase the ratio at which the first image data is blended as the similarity between the position of the moving object of the first image data and the position of the moving object of the second image data is lower.

[0054] In operation 304, at least one processor (110) can perform temporal noise reduction of the first image data by blending the first image data and the second image data according to the temporal weight data. At least one processor (110) can acquire a third image data based on performing the temporal noise reduction of the first image data. For example, the operation of blending the first image data and the second image data can be performed with respect to the temporal noise reduction of the first image data. For example, the temporal noise reduction of the first image data may include an operation of correcting (or compensating for) (or determining) the value of the first pixel of the first image data using the value of the second pixel of the second image data. For example, the location of the first pixel and the location of the second pixel may correspond to each other. For example, the pixel value of the third image data can be described as the sum of the value of the first pixel of the first image data multiplied by 'a' and the value of the second pixel of the second image data multiplied by '1-a'. The third image data can be described as image data with reduced noise compared to the first image data.

[0055] In operation 305, at least one processor (110) can identify spatial weight data for spatial noise reduction of the first image data according to the third image data. For example, the spatial weight data can be obtained by comparing a pixel of the third image data with at least one pixel of the third image data adjacent to the pixel of the third image data. For example, at least one processor (110) can identify similarity (or difference) between pixels within the third image data by comparing a pixel of the third image data with at least one pixel of the third image data adjacent to the pixel of the third image data. For example, the method of identifying the similarity (or difference) between the pixels can be described in various ways according to the embodiment. For example, the method can be described as pixel difference (PD), sum of absolute difference (SAD), or normalized cross-correlation (NCC). For example, at least one processor (110) may obtain spatial weight data based on the similarity (or difference) between the pixels within the third image data. For example, the spatial weight data may indicate a higher spatial weight as the similarity increases.

[0056] In operation 306, at least one processor (110) can perform spatial noise reduction of the first image data by applying the spatial weight data to the first image data. At least one processor (110) can acquire a fourth image data based on performing the spatial noise reduction of the first image data. For example, the operation of applying the spatial weight data acquired according to the third image data to the first image data can be performed with respect to the spatial noise reduction of the first image data. For example, the spatial noise reduction of the first image data may include an operation of correcting (or compensating for) (or determining) the value of the first pixel of the first image data using at least one value of at least one pixel of the third image data adjacent to the second pixel of the third image data. For example, the location of the first pixel and the location of the second pixel may correspond to each other. For example, at least one processor (110) can obtain the fourth image data by applying the spatial weight data obtained according to the third image data to the first image data. For example, the pixel value of the fourth image data can be described as the weighted mean of the pixel values ​​of the first image data to which the spatial weight data is applied. For example, the fourth image data can be described as image data with reduced noise compared to the first image data.

[0057] FIG. 4 illustrates an example of an environment for identifying weights for spatial noise reduction of an image performed within an electronic device.

[0058] Referring to FIG. 4, an environment (400) is illustrated for identifying spatial weight data for spatial noise reduction based on third image data in which first image data and second image data are blended according to temporal weight data. The first image data is described as image data on which the spatial noise reduction is to be performed. For example, the third image data may be described as image data (401). Image data (401) includes a first region (410) and a second region (420). The first region (410) includes pixels (p11), (p12), (p13), (p14), (p15), (p16), (p17), (p18), and (p19). The second region (420) includes pixels (p21), pixels (p22), pixels (p23), pixels (p24), pixels (p25), pixels (p26), pixels (p27), pixels (p28), and pixels (p29).

[0059] The electronic device (101) can identify similarity (or difference) between pixels within the third image data by comparing a pixel of the third image data with at least one pixel of the third image data adjacent to the pixel of the third image data. For example, the electronic device (101) can identify similarity (or difference) between pixels within the image data (401) by comparing a pixel (p15) of the image data (401) with a pixel (p25) adjacent to the pixel (p15) of the image data (401).

[0060] The electronic device (101) can identify the similarity (or difference) between the pixels within the third image data by comparing a first region containing the pixel with a second region containing at least one pixel adjacent to the pixel, in order to increase the accuracy of identifying the similarity (or difference) between the pixels within the third image data. For example, the electronic device (101) can identify the similarity (or difference) between the pixels within the third image data by comparing a first region (410) containing the pixel (p15) with a second region (420) containing the pixel (p25) adjacent to the pixel (p15).

[0061] The electronic device (101) can identify similarity (or difference) between pixels based on the difference between the values ​​of pixels included in the first region and the values ​​of pixels included in the second region. The electronic device (101) can identify spatial weight data based on applying a value representing the similarity (or difference) to a defined function. For example, the electronic device (101) can identify similarity (or difference) between pixels of image data (401) through a sum of absolute difference (SAD) method within image data (401). For example, the electronic device (101) can identify a first difference between the value of pixel (p11) and the value of pixel (p21), a second difference between the value of pixel (p12) and the value of pixel (p22), a third difference between the value of pixel (p13) and the value of pixel (p23), a fourth difference between the value of pixel (p14) and the value of pixel (p24), a fifth difference between the value of pixel (p15) and the value of pixel (p25), a sixth difference between the value of pixel (p16) and the value of pixel (p26), a seventh difference between the value of pixel (p17) and the value of pixel (p27), an eighth difference between the value of pixel (p18) and the value of pixel (p28), and a ninth difference between the value of pixel (p19) and the value of pixel (p29). For example, the electronic device (101) can identify a sum value obtained by adding the first difference, the second difference, the third difference, the fourth difference, the fifth difference, the sixth difference, the seventh difference, the eighth difference, and the ninth difference. For example, the electronic device (101) can identify spatial weight data including spatial weights based on applying the sum value to a defined function. For example, the value of the spatial weight can increase as the difference between the pixel values ​​becomes smaller through the defined function (e.g., exponential function).

[0062] The electronic device (101) can obtain the fourth image data by applying the spatial weight data to the first image data on which the spatial noise reduction is to be performed. For example, the electronic device (101) can obtain the fourth image data using a weighted mean obtained by dividing the sum of the values ​​obtained by multiplying the spatial weights included in the spatial weight data by the pixel values ​​of the first image data by the sum of the spatial weights.

[0063] Figure 5 is a diagram illustrating an image obtained according to noise reduction of an image performed within an electronic device.

[0064] Referring to FIG. 5, an environment (500) is shown comprising an image (510) obtained according to noise reduction of an image performed by an operation method based on the present disclosure, and a comparative example image (520) obtained according to noise reduction of an image performed by an operation method different from the present disclosure. The image (510) is described as a result image in which spatial weighting data obtained according to third image data obtained by blending first image data and second image data is applied to said first image data. The image (520) is described as a comparative example image obtained according to noise reduction of an image performed by an operation method different from the present disclosure.

[0065] Referring to image (510), the electronic device (101) according to the present disclosure applies the spatial weighting data to the first image data, which is an unblended image, so that the change in detail (e.g., sharpness) of an object (511) (e.g., a tree branch) of the image (510) can be minimized by performing noise reduction. Referring to image (520), in the case of a method of operation different from the present disclosure, it can be seen that the change in detail (e.g., sharpness) of an object (521) (e.g., a tree branch) of the image (520) is achieved by performing noise reduction.

[0066] Referring to image (510), the electronic device (101) according to the present disclosure identifies the spatial weight data according to the third image data, which has reduced noise compared to the first image data, so that the accuracy of calculating similarity between pixels can be mitigated even if the first image data corresponds to a low-light image and / or an image having a high noise level. For example, the electronic device (101) according to the present disclosure can increase the amount of noise removed within the area (512) (e.g., sky) of the image (510). Referring to image (520), in the case of a method of operation different from the present disclosure, it can be seen that noise is not removed within the area (522) (e.g., sky) of the image (520).

[0067] Figure 6 illustrates an example of an environment in which noise reduction of an image is performed within an electronic device.

[0068] Referring to FIG. 6, an environment (600) in which noise reduction of an image is performed within an electronic device (101) is illustrated. The environment (600) may include a temporal weight generator (610), an image blender (620), a spatial noise reducer (630), and a TNR (temporal noise reduction) filter (640). The spatial noise reducer (630) may include a spatial weight generator (631) and a SNR (spatial noise reduction) filter (632). Each of the temporal weight generator (610), the image blender (620), the spatial weight generator (631), the SNR filter (632), and the TNR filter (640) may be described as a program executed by at least one processor (110) in relation to noise reduction of an image. For example, the programs may be stored in memory (120).

[0069] The electronic device (101) can perform noise reduction to reduce noise present in an image through image processing. For example, the noise reduction may include temporal noise reduction that blends two or more images and spatial noise reduction that corrects the image according to the similarity between pixels within a single image.

[0070] In the environment (600), the electronic device (101) can perform noise reduction of the first image data (image1) based on sequentially performing a first temporal noise reduction, a spatial noise reduction, and a second temporal noise reduction on the first image data (image1). For example, the first temporal noise reduction can be performed through a temporal weight generation unit (610) and an image blender (620) in the environment (600). For example, the spatial noise reduction can be performed through a spatial weight generation unit (631) and an SNR filter (632) in the environment (600). For example, the second temporal noise reduction can be performed through a TNR filter (640) in the environment (600).

[0071] The electronic device (101) can identify two or more images that are sequentially acquired through at least one camera (130). For example, the two or more images may include a first image and a second image. For example, the second image may be described as a previous image of the first image acquired before the first image was acquired through at least one camera (130). For example, the first image may be described as the 'N'th ('N' is a natural number greater than or equal to 2) video frame among the video frames acquired through at least one camera (130). For example, the second image may be described as the 'N-1'th video frame among the video frames acquired through at least one camera (130). For example, the ordinal number of the video frames may be determined according to the order in which they are acquired through the camera (130). For example, the first image and the second image, respectively, can each be described as raw data in which noise reduction has not been performed.

[0072] The electronic device (101) can acquire a first image data (image1). The first image data (image1) may correspond to the first image acquired through at least one camera (130). For example, the first image data (image1) may be described as image data in which the noise reduction is performed within the environment (600).

[0073] The electronic device (101) can identify a second image data (image2) obtained according to the noise reduction performed before the noise reduction of the first image data (image1) is performed, based on acquiring the first image data (image1). The second image data (image2) may correspond to the second image obtained through at least one camera (130) before the first image is acquired. For example, the second image data (image2) may be described as image data in which at least a portion of the noise reduction is performed within the environment (600). For example, the second image data (image2) may be used for the noise reduction of the first image data (image1) within the environment (600).

[0074] For example, since the second image data (image2) is image data in which at least a portion of the noise reduction is performed within the environment (600), the first noise difference between the first image and the first image data (image1) may be smaller than the second noise difference between the second image and the second image data (image2).

[0075] In one embodiment, each of the first image data (image1) and the second image data (image2) may be image data in which at least one noise reduction is performed, distinct from the noise reduction performed within the environment (600). In one embodiment, each of the first image data (image1) and the second image data (image2) may be image data in which at least one noise reduction is not performed, distinct from the noise reduction performed within the environment (600). For example, since the second image data (image2) is image data in which at least a portion of the noise reduction is performed within the environment (600), the number of noise reductions performed on the first image data (image1) may be smaller than the number of noise reductions performed on the second image data (image2).

[0076] The electronic device (101) can identify a first temporal weight data (W_TNR) for the first temporal noise reduction of the first image data (image1) based on comparing the first image data (image1) with the second image data (image2) to perform moving object detection through the temporal weight generation unit (610). The second image data (image2) input through the temporal weight generation unit (610) can be described as image data in which at least a portion of the noise reduction has been performed within the environment (600). For example, the second image data (image2) input through the temporal weight generation unit (610) may be image data in which the first temporal noise reduction, the spatial noise reduction, and the second temporal noise reduction have been performed within the environment (600). As another example, the second image data (image2) input through the temporal weight generation unit (610) may be image data in which the first temporal noise reduction and the spatial noise reduction have been performed within the environment (600). As yet another example, the second image data (image2) input through the temporal weight generation unit (610) may be image data in which the first temporal noise reduction has been performed within the environment (600).

[0077] For example, the electronic device (101) may perform the moving object detection based on identifying the similarity (or difference) between the position of the moving object in the first image data (image1) and the position of the moving object in the second image data (image2) through the temporal weight generation unit (610). For example, the second image data (image2) may be image data in which the position of the moving object in the second image data (image2) is compensated (or warped) according to the position of the moving object in the first image data (image1). However, the present disclosure is not necessarily limited thereto. For example, in order to increase the speed of processing the noise reduction within the environment (600) and reduce the power consumed according to the noise reduction, the electronic device (101) may perform the moving object detection using the second image data (image2) in which the position of the moving object is not compensated (or warped) through the temporal weight generation unit (610). For example, the method for identifying the similarity (or difference) for the above-mentioned moving object detection can be described in various ways depending on the embodiment. For example, the method can be described as PD (pixel difference), SAD (sum of absolute difference), or NCC (normalized cross-correlation).

[0078] For example, the electronic device (101) can identify a first temporal weight data (W_TNR) based on performing the moving object detection through the temporal weight generation unit (610). For example, the first temporal weight data (W_TNR) may represent a ratio (e.g., a:1-a) for blending the first image data (image1) and the second image data (image2). For example, 'a' may be a weight applied to the first image data (image1). For example, '1-a' may be a weight applied to the second image data (image2). For example, 'a' may be a number between 0 and 1. For example, the electronic device (101) may lower 'a' to increase the blending ratio of the second image data (image2) as the similarity between the position of the moving object in the first image data (image1) and the position of the moving object in the second image data (image2) is higher. For example, the electronic device (101) may increase 'a' to increase the blending ratio of the first image data (image1) as the similarity between the position of the moving object in the first image data (image1) and the position of the moving object in the second image data (image2) is lower. For example, the electronic device (101) may mitigate the ghosting phenomenon caused by the second image data (image2) by increasing 'a' as the similarity is lower.

[0079] The electronic device (101) can perform the first temporal noise reduction of the first image data (image1) by blending the first image data (image1) and the second image data (image2) according to the first temporal weight data (W_TNR) through the image blender (620). The electronic device (101) can obtain the third image data (image3) based on performing the first temporal noise reduction of the first image data (image1) through the image blender (620). The second image data (image2) input through the image blender (620) can be described as image data in which at least a portion of the noise reduction has been performed within the environment (600). For example, the second image data (image2) input through the image blender (620) may be image data in which the first temporal noise reduction, the spatial noise reduction, and the second temporal noise reduction are performed within the environment (600). As another example, the second image data (image2) input through the image blender (620) may be image data in which the first temporal noise reduction and the spatial noise reduction are performed within the environment (600). As yet another example, the second image data (image2) input through the image blender (620) may be image data in which the first temporal noise reduction is performed within the environment (600). For example, the second image data (image2) may have the same resolution as the first image data (image1) or a lower resolution than the first image data (image1), depending on the embodiment.For example, if the second image data (image2) has a lower resolution than the first image data (image1), the electronic device (101) can adjust the resolution of the second image data (image2) to be the same as the resolution of the first image data (image1) through the image blender (620).

[0080] For example, the operation of blending the first image data (image1) and the second image data (image2) may be performed with respect to the first temporal noise reduction of the first image data (image1). For example, the first temporal noise reduction of the first image data (image1) may include the operation of correcting (or compensating for) (or determining) the value of the first pixel of the first image data (image1) using the value of the second pixel of the second image data (image2). For example, the location of the first pixel and the location of the second pixel may correspond to each other. For example, the electronic device (101) may obtain the third image data (image3) by blending the first image data (image1) and the second image data (image2) according to the first temporal weight data (W_TNR). For example, the pixel value of the third image data (image3) can be described as the sum of the value of the first pixel of the first image data (image1) multiplied by 'a' and the value of the second pixel of the second image data (image2) multiplied by '1-a'. The third image data (image3) can be described as image data with reduced noise compared to the first image data (image1).

[0081] The electronic device (101) can identify spatial weight data (W_SNR) for spatial noise reduction of the first image data (image1) according to the third image data (image3) through the spatial weight generation unit (631). For example, the electronic device (101) can identify similarity (or difference) between pixels within the third image data (image3) by comparing a pixel of the third image data (image3) with at least one pixel of the third image data (image3) adjacent to said pixel of the third image data (image3) through the spatial weight generation unit (631). For example, the method of identifying said similarity (or difference) between said pixels can be described in various ways according to the embodiment. For example, said method can be described as PD (pixel difference), SAD (sum of absolute difference), or NCC (normalized cross-correlation). For example, the electronic device (101) can obtain spatial weight data (W_SNR) based on the similarity (or difference) between the pixels within the third image data (image3). For example, the spatial weight data (W_SNR) may indicate a higher spatial weight as the similarity increases.

[0082] The electronic device (101) can perform spatial noise reduction of the first image data (image1) by applying spatial weight data (W_SNR) to the first image data (image1) through an SNR filter (632). The electronic device (101) can acquire the fourth image data (image4) based on performing the spatial noise reduction of the first image data (image1) through an SNR filter (632). For example, the operation of applying spatial weight data (W_SNR) acquired according to the third image data (image3) to the first image data (image1) can be performed with respect to the spatial noise reduction of the first image data (image1). For example, the spatial noise reduction of the first image data (image1) may include an operation of correcting (or compensating for) (or determining) the value of the first pixel of the first image data using at least one value of at least one pixel of the third image data adjacent to the second pixel of the third image data. For example, the location of the first pixel and the location of the second pixel may correspond to each other. For example, the electronic device (101) may obtain the fourth image data (image4) by applying spatial weight data (W_SNR) obtained according to the third image data (image3) to the first image data (image1). For example, the value of the pixel of the fourth image data (image4) may be described as the weighted mean of the values ​​of the pixels of the first image data (image1) to which the spatial weight data (W_SNR) is applied. For example, the SNR filter (632) can be described as a bilateral filter using a weighted average or a non-local means (NLM) filter using a weighted average.The fourth image data (image4) can be described as image data with reduced noise compared to the first image data (image1).

[0083] The electronic device (101) can obtain difference data (data_diff) regarding the amount of noise by comparing the fourth image data (image4) with the first image data (image1) through the SNR filter (632). For example, the difference data (data_diff) can be described as map data representing the change in noise levels between the first image data (image1) and the fourth image data (image4) as a result of performing the spatial noise reduction.

[0084] In one embodiment, the electronic device (101) can perform spatial noise reduction of the first image data (image1) using the first temporal weight data (W_TNR) through the SNR filter (632).

[0085] For example, the electronic device (101) can mitigate the decrease in accuracy of calculating spatial weights even if the first image data (image1) corresponds to a low-light image and / or an image having a high noise level by identifying spatial weight data (W_SNR) for spatial noise reduction based on a third image data (image3) in which the first image data (image1) and the second image data (image2) are blended according to the first temporal noise reduction. For example, the electronic device (101) can increase the amount of noise removed through the spatial noise reduction.

[0086] For example, the electronic device (101) can minimize changes in image details as a result of performing noise reduction by applying spatial weight data (W_SNR) identified according to the third image data (image3) to the first image data (image1), which is an unblended image.

[0087] The electronic device (101) can perform the second temporal noise reduction of the first image data (image1) by blending the second image data (image2) and the fourth image data (image4) according to the second temporal weight data (W_TNRa) through the TNR filter (640). The electronic device (101) can obtain the fifth image data (image5) based on performing the second temporal noise reduction of the first image data (image1) through the TNR filter (640). The second image data (image2) input through the TNR filter (640) can be described as image data in which at least a portion of the noise reduction has been performed within the environment (600). For example, the second image data (image2) input through the TNR filter (640) may be image data in which the first temporal noise reduction, the spatial noise reduction, and the second temporal noise reduction have been performed within the environment (600). As another example, the second image data (image2) input through the TNR filter (640) may be image data in which the first temporal noise reduction and the spatial noise reduction have been performed within the environment (600). As yet another example, the second image data (image2) input through the TNR filter (640) may be image data in which the first temporal noise reduction has been performed within the environment (600).

[0088] The second temporal weight data (W_TNRa) can be used to blend the fourth image data (image4) and the second image data (image2). For example, the second temporal weight data (W_TNRa) may represent a ratio (e.g., b:1-b) for blending the fourth image data (image4) and the second image data (image2). For example, 'b' may be a weight applied to the fourth image data (image4). For example, '1-b' may be a weight applied to the second image data (image2). For example, 'b' may be a number between 0 and 1.

[0089] The second temporal weight data (W_TNRa) can be obtained based on the first temporal weight data (W_TNR). In one embodiment, the second temporal weight data (W_TNRa) may correspond to the first temporal weight data (W_TNR). For example, 'b' represented by the second temporal weight data (W_TNRa) may be the same as 'a' represented by the first temporal weight data (W_TNR). In one embodiment, the second temporal weight data (W_TNRa) may be different from the first temporal weight data (W_TNR). For example, 'b' represented by the second temporal weight data (W_TNRa) may be different from 'a' represented by the first temporal weight data (W_TNR).

[0090] The electronic device (101) can adjust the second temporal weight data (W_TNRa) according to the difference data (data_diff) through the TNR filter (640). The electronic device (101) can obtain the fifth image data (image5) by blending the second image data (image2) and the fourth image data (image4) according to the adjusted second temporal weight data (W_TNRa). For example, the electronic device (101) can improve the noise distribution of the fifth image data (image5) by adjusting the second temporal weight data (W_TNRa) according to the difference data (data_diff).

[0091] For example, the operation of blending the fourth image data (image4) and the second image data (image2) may be performed with respect to the second temporal noise reduction of the first image data (image1). For example, the second temporal noise reduction of the first image data (image1) may include an operation of correcting (or compensating for) (or determining) the value of the first pixel of the fourth image data (image4) using the value of the second pixel of the second image data (image2). For example, the location of the first pixel and the location of the second pixel may correspond to each other. For example, the electronic device (101) may obtain the fifth image data (image5) by blending the fourth image data (image4) and the second image data (image2) according to the second temporal weight data (W_TNRa). For example, the pixel value of the fifth image data (image5) can be described as the sum of the value of the first pixel of the fourth image data (image4) multiplied by 'b' and the value of the second pixel of the second image data (image2) multiplied by '1-b'. The fifth image data (image5) can be described as image data with reduced noise compared to the fourth image data (image4). For example, the electronic device (101) can store the fifth image data (image5) as a final image in memory (120) in relation to gallery application software. As another example, the electronic device (101) can perform a different noise reduction on the fifth image data (image5) that is distinct from the noise reduction performed within the environment (600).

[0092] Figure 7 illustrates an example of an environment in which noise reduction of an image is performed within an electronic device.

[0093] Referring to FIG. 7, an environment (700) in which noise reduction of an image is performed within an electronic device (101) is illustrated. The environment (700) may include a temporal weight generator (710), a spatial noise reducer (730), and an image blender (740). The spatial noise reducer (730) may include a spatial weight generator (731) and a spatial noise reduction (SNR) filter (732). Each of the temporal weight generator (710), the spatial weight generator (731), the SNR filter (732), and the image blender (740) may be described as a program executed by at least one processor (110) in relation to noise reduction of an image. For example, the programs may be stored in memory (120).

[0094] The electronic device (101) can perform noise reduction to reduce noise present in an image through image processing. For example, the noise reduction may include temporal noise reduction that blends two or more images and spatial noise reduction that corrects the image according to the similarity between pixels within a single image.

[0095] In the environment (700), the electronic device (101) can perform noise reduction of the first image data (image1) based on sequentially performing spatial noise reduction and temporal noise reduction on the first image data (image1). For example, the spatial noise reduction can be performed through a spatial weight generation unit (731) and an SNR filter (732) in the environment (700). For example, the temporal noise reduction can be performed through a temporal weight generation unit (710) and an image blender (740) in the environment (700).

[0096] The electronic device (101) can identify two or more images that are sequentially acquired through at least one camera (130). For example, the two or more images may include a first image and a second image. For example, the second image may be described as a previous image of the first image acquired before the first image was acquired through at least one camera (130). For example, the first image may be described as the 'N'th ('N' is a natural number greater than or equal to 2) video frame among the video frames acquired through at least one camera (130). For example, the second image may be described as the 'N-1'th video frame among the video frames acquired through at least one camera (130). For example, the ordinal number of the video frames may be determined according to the order in which they are acquired through the camera (130). For example, the first image and the second image, respectively, can each be described as raw data in which noise reduction has not been performed.

[0097] The electronic device (101) can acquire a first image data (image1). The first image data (image1) may correspond to the first image acquired through at least one camera (130). For example, the first image data (image1) may be described as image data in which the noise reduction is performed within the environment (700).

[0098] The electronic device (101) can identify a second image data (image2) obtained according to the noise reduction performed before the noise reduction of the first image data (image1) is performed, based on acquiring the first image data (image1). The second image data (image2) may correspond to the second image obtained through at least one camera (130) before the first image is acquired. For example, the second image data (image2) may be described as image data in which at least a portion of the noise reduction is performed within the environment (700). For example, the second image data (image2) may be used for the noise reduction of the first image data (image1) within the environment (700).

[0099] For example, since the second image data (image2) is image data in which at least a portion of the noise reduction is performed within the environment (700), the first noise difference between the first image and the first image data (image1) may be smaller than the second noise difference between the second image and the second image data (image2).

[0100] In one embodiment, each of the first image data (image1) and the second image data (image2) may be image data in which at least one noise reduction is performed, distinct from the noise reduction performed within the environment (700). In one embodiment, each of the first image data (image1) and the second image data (image2) may be image data in which at least one noise reduction is not performed, distinct from the noise reduction performed within the environment (700). For example, since the second image data (image2) is image data in which at least a portion of the noise reduction is performed within the environment (700), the number of noise reductions performed on the first image data (image1) may be smaller than the number of noise reductions performed on the second image data (image2).

[0101] The electronic device (101) can identify temporal weight data (W_TNR) for temporal noise reduction of the first image data (image1) based on comparing the first image data (image1) with the second image data (image2) to perform moving object detection through the temporal weight generation unit (710). The second image data (image2) input through the temporal weight generation unit (710) can be described as image data in which at least a portion of the noise reduction has been performed within the environment (700). For example, the second image data (image2) input through the temporal weight generation unit (710) may be image data in which the spatial noise reduction and the temporal noise reduction have been performed within the environment (700). As another example, the second image data (image2) input through the temporal weight generation unit (710) may be image data in which the spatial noise reduction is performed within the environment (700).

[0102] For example, the electronic device (101) may perform the moving object detection based on identifying the similarity (or difference) between the position of the moving object in the first image data (image1) and the position of the moving object in the second image data (image2) through the temporal weight generation unit (710). For example, the second image data (image2) may be image data in which the position of the moving object in the second image data (image2) is compensated (or warped) according to the position of the moving object in the first image data (image1). However, the present disclosure is not necessarily limited thereto. For example, in order to increase the speed of processing the noise reduction within the environment (700) and reduce the power consumed according to the noise reduction, the electronic device (101) may perform the moving object detection using the second image data (image2) in which the position of the moving object is not compensated (or warped) through the temporal weight generation unit (710). For example, the method for identifying the similarity (or difference) for the above-mentioned moving object detection can be described in various ways depending on the embodiment. For example, the method can be described as PD (pixel difference), SAD (sum of absolute difference), or NCC (normalized cross-correlation).

[0103] For example, the electronic device (101) can identify temporal weight data (W_TNR) based on performing the moving object detection through the temporal weight generation unit (710). The temporal weight data (W_TNR) can be used to blend the fourth image data (image4) and the second image data (image2).

[0104] The electronic device (101) can identify a first spatial weight data (W_SNR1) for spatial noise reduction of the first image data (image1) according to the first image data (image1) through the spatial weight generation unit (731). For example, the electronic device (101) can identify similarity (or difference) between pixels within the first image data (image1) by comparing a pixel of the first image data (image1) with at least one pixel of the first image data (image1) adjacent to said pixel of the first image data (image1) through the spatial weight generation unit (731). For example, the method of identifying said similarity (or difference) between said pixels can be described in various ways according to the embodiment. For example, said method can be described as PD (pixel difference), SAD (sum of absolute difference), or NCC (normalized cross-correlation). For example, an electronic device (101) can obtain a first spatial weight data (W_SNR1) based on the similarity (or difference) between the pixels within the first image data (image1). For example, the first spatial weight data (W_SNR1) may indicate a higher spatial weight as the similarity increases.

[0105] The electronic device (101) can identify a second spatial weight data (W_SNR2) for spatial noise reduction of the first image data (image1) according to the second image data (image2) through the spatial weight generation unit (731). For example, the electronic device (101) can identify similarity (or difference) between pixels within the second image data (image2) by comparing a pixel of the second image data (image2) with at least one pixel of the second image data (image2) adjacent to said pixel of the second image data (image2) through the spatial weight generation unit (731). For example, the method of identifying said similarity (or difference) between said pixels can be described in various ways according to the embodiment. For example, said method can be described as PD (pixel difference), SAD (sum of absolute difference), or NCC (normalized cross-correlation). For example, the electronic device (101) can obtain second spatial weight data (W_SNR2) based on the similarity (or difference) between the pixels within the second image data (image2). For example, the second spatial weight data (W_SNR2) may indicate a higher spatial weight as the similarity increases.

[0106] The electronic device (101) can perform spatial noise reduction of the first image data (image1) by applying the first spatial weight data (W_SNR1) and the second spatial weight data (W_SNR2) to the first image data (image1) through the SNR filter (732). The electronic device (101) can obtain the fourth image data (image4) based on performing the spatial noise reduction of the first image data (image1) through the SNR filter (732). For example, the electronic device (101) can obtain composite spatial weight data by synthesizing the first spatial weight data (W_SNR1) and the second spatial weight data (W_SNR2) through the SNR filter (732). For example, the electronic device (101) can apply the composite spatial weight data to the first image data (image1) through the SNR filter (732). For example, the operation of applying the first spatial weight data (W_SNR1) and the second spatial weight data (W_SNR2) to the first image data (image1) can be performed with respect to the spatial noise reduction of the first image data (image1). For example, the pixel value of the fourth image data (image4) can be described as the weighted mean of the pixel values ​​of the first image data (image1) to which the synthetic spatial weight data is applied. For example, the SNR filter (732) can be described as a bilateral filter using a weighted mean or a non-local means (NLM) filter using a weighted mean. The fourth image data (image4) can be described as image data with reduced noise compared to the first image data (image1).

[0107] In one embodiment, the electronic device (101) can perform spatial noise reduction of the first image data (image1) using temporal weight data (W_TNR) through an SNR filter (732).

[0108] The electronic device (101) can perform the temporal noise reduction of the first image data (image1) by blending the second image data (image2) and the fourth image data (image4) according to the temporal weight data (W_TNR) through the image blender (740). The electronic device (101) can obtain the fifth image data (image5) based on performing the temporal noise reduction of the first image data (image1) through the image blender (740). The second image data (image2) input through the image blender (740) can be described as image data in which at least a portion of the noise reduction has been performed within the environment (700). For example, the second image data (image2) input through the image blender (740) may be image data in which the spatial noise reduction and the temporal noise reduction have been performed within the environment (700). As another example, the second image data (image2) input through the image blender (740) may be image data in which the spatial noise reduction is performed within the environment (700). The fifth image data (image5) may be described as image data with reduced noise compared to the fourth image data (image4). For example, the electronic device (101) may store the fifth image data (image5) as a final image in memory (120) in relation to gallery application software. As another example, the electronic device (101) may perform a different noise reduction on the fifth image data (image5) that is distinct from the noise reduction performed within the environment (700).

[0109] The electronic device (101) may correspond to the electronic device (801) described with reference to FIG. 8 below.

[0110] FIG. 8 is a block diagram of an electronic device in a network environment according to various embodiments.

[0111] Referring to FIG. 8, in a network environment (800), an electronic device (801) may communicate with an electronic device (802) through a first network (898) (e.g., a short-range wireless communication network) or with at least one of an electronic device (804) or a server (808) through a second network (899) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (801) may communicate with the electronic device (804) through a server (808). According to one embodiment, the electronic device (801) may include a processor (820), memory (830), input module (850), sound output module (855), display module (860), audio module (870), sensor module (876), interface (877), connection terminal (878), haptic module (879), camera module (880), power management module (888), battery (889), communication module (890), subscriber identification module (896), or antenna module (897). In some embodiments, at least one of these components (e.g., connection terminal (878)) may be omitted from the electronic device (801), or one or more other components may be added. In some embodiments, some of these components (e.g., sensor module (876), camera module (880), or antenna module (897)) may be integrated into a single component (e.g., display module (860)).

[0112] The processor (820) can control at least one other component (e.g., a hardware or software component) of the electronic device (801) connected to the processor (820) by executing software (e.g., a program (840)), for example, and can perform various data processing or operations. According to one embodiment, as at least part of the data processing or operations, the processor (820) can store commands or data received from other components (e.g., a sensor module (876) or a communication module (890)) in volatile memory (832), process the commands or data stored in volatile memory (832), and store the resulting data in non-volatile memory (834). According to one embodiment, the processor (820) may include a main processor (821) (e.g., a central processing unit or an application processor) or an auxiliary processor (823) that can operate independently or together with it (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, if the electronic device (801) includes a main processor (821) and an auxiliary processor (823), the auxiliary processor (823) may be configured to use lower power than the main processor (821) or to be specialized for a designated function. The auxiliary processor (823) may be implemented separately from the main processor (821) or as part thereof.

[0113] The auxiliary processor (823) may control at least some of the functions or states associated with at least one component of the electronic device (801) (e.g., display module (860), sensor module (876), or communication module (890)) on behalf of the main processor (821) while the main processor (821) is in an inactive (e.g., sleep) state, or together with the main processor (821) while the main processor (821) is in an active (e.g., application execution) state. According to one embodiment, the auxiliary processor (823) (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module (880) or communication module (890)). According to one embodiment, the auxiliary processor (823) (e.g., neural network processing unit) may include a hardware structure specialized for processing an artificial intelligence model. The artificial intelligence model may be generated through machine learning. Such learning may be performed, for example, on the electronic device (801) itself where the artificial intelligence model is executed, or through a separate server (e.g., server (808)). The learning algorithm may 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 may include a plurality of artificial neural network layers.An artificial neural network may be 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 the hardware structure, the artificial intelligence model may include a software structure, either additionally or substantially.

[0114] The memory (830) can store various data used by at least one component of the electronic device (801) (e.g., processor (820) or sensor module (876)). The data may include, for example, software (e.g., program (840)) and input or output data for related commands. The memory (830) may include volatile memory (832) or non-volatile memory (834).

[0115] The program (840) may be stored as software in memory (830) and may include, for example, an operating system (842), middleware (844), or an application (846).

[0116] The input module (850) can receive commands or data to be used for a component of the electronic device (801) (e.g., processor (820)) from outside the electronic device (801) (e.g., user). The input module (850) may include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0117] The sound output module (855) can output an audio signal to the outside of the electronic device (801). The sound output module (855) may include, for example, a speaker or a receiver. The speaker may be used for general purposes, such as multimedia playback or recording playback. The receiver may be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part thereof.

[0118] The display module (860) can visually provide information to an external (e.g., user) of the electronic device (801). The display module (860) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling said device. According to one embodiment, the display module (860) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of the force generated by said touch.

[0119] The audio module (870) can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module (870) can acquire sound through the input module (850) or output sound through the sound output module (855) or an external electronic device (e.g., electronic device (802)) (e.g., speaker or headphones) connected directly or wirelessly to the electronic device (801).

[0120] The sensor module (876) can detect the operating state of the electronic device (801) (e.g., power or temperature) or the external environmental state (e.g., user state) and generate an electrical signal or data value corresponding to the detected state. According to one embodiment, the sensor module (876) may include, for example, a gesture sensor, a gyroscope sensor, a barometric pressure sensor, a magnetic sensor, an accelerometer sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biosensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0121] The interface (877) may support one or more specified protocols that can be used for the electronic device (801) to be connected directly or wirelessly to an external electronic device (e.g., electronic device (802)). According to one embodiment, the interface (877) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0122] The connection terminal (878) may include a connector through which the electronic device (801) can be physically connected to an external electronic device (e.g., electronic device (802)). According to one embodiment, the connection terminal (878) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0123] The haptic module (879) can convert an electrical signal into a mechanical stimulus (e.g., vibration or movement) or an electrical stimulus that can be perceived by the user through tactile or kinesthetic senses. According to one embodiment, the haptic module (879) may include, for example, a motor, a piezoelectric element, or an electric stimulation device.

[0124] The camera module (880) can capture still images and video. According to one embodiment, the camera module (880) may include one or more lenses, image sensors, image signal processors, or flashes.

[0125] The power management module (888) can manage power supplied to the electronic device (801). According to one embodiment, the power management module (888) may be implemented, for example, as at least part of a power management integrated circuit (PMIC).

[0126] The battery (889) can supply power to at least one component of the electronic device (801). According to one embodiment, the battery (889) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0127] The communication module (890) can support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between an electronic device (801) and an external electronic device (e.g., electronic device (802), electronic device (804), or server (808)), and the performance of communication through the established communication channel. The communication module (890) may include one or more communication processors that operate independently of the processor (820) (e.g., application processor) and support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (890) may include a wireless communication module (892) (e.g., cellular communication module, short-range wireless communication module, or GNSS (global navigation satellite system) communication module) or a wired communication module (894) (e.g., LAN (local area network) communication module, or power line communication module). The corresponding communication module among these communication modules can communicate with an external electronic device (804) through a first network (898) (e.g., a short-range communication network such as Bluetooth, WiFi (wireless fidelity) direct, or IrDA (infrared data association)) or a second network (899) (e.g., a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). 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 (892) can identify or authenticate the electronic device (801) within a communication network such as the first network (898) or the second network (899) using subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module (896).

[0128] The wireless communication module (892) can support 5G networks and next-generation communication technologies following 4G networks, for example, new radio access technology. NR access technology can support high-speed transmission of high-capacity data (enhanced mobile broadband (eMBB)), minimization of terminal power and connection of multiple terminals (massive machine type communications (mMTC)), or high reliability and low latency (ultra-reliable and low-latency communications (URLLC)). The wireless communication module (892) can support a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate, for example. The wireless communication module (892) can support various technologies for securing performance in the high-frequency band, such as beamforming, massive MIMO (multiple-input and multiple-output), full-dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large-scale antenna. The wireless communication module (892) can support various requirements specified in the electronic device (801), external electronic device (e.g., electronic device (804)), or network system (e.g., second network (899)). According to one embodiment, the wireless communication module (892) may support a Peak data rate (e.g., 20 Gbps or more) for eMBB realization, loss coverage (e.g., 164 dB or less) for mMTC realization, or U-plane latency (e.g., downlink (DL) and uplink (UL) each 0.5 ms or less, or round trip 1 ms or less) for URLLC realization.

[0129] An antenna module (897) can transmit a signal or power to or from an external source (e.g., an external electronic device). According to one embodiment, the antenna module (897) may include an antenna comprising a radiator made of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). According to one embodiment, the antenna module (897) 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 a first network (898) or a second network (899), may be selected from the plurality of antennas, for example, by a communication module (890). A signal or power may be transmitted or received between the communication module (890) and an external electronic device through the selected at least one antenna. According to some embodiments, in addition to the radiator, other components (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as part of the antenna module (897).

[0130] According to various embodiments, the antenna module (897) may form a mmWave antenna module. According to one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent to a first surface (e.g., bottom surface) of the printed circuit board and capable of supporting a specified high frequency band (e.g., mmWave band), and a plurality of antennas (e.g., array antennas) disposed on or adjacent to a second surface (e.g., top surface or side surface) of the printed circuit board and capable of transmitting or receiving a signal of the specified high frequency band.

[0131] At least some of the above components can be connected to each other via a communication method between peripheral devices (e.g., bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)) and exchange signals (e.g., commands or data) with each other.

[0132] According to one embodiment, commands or data may be transmitted or received between an electronic device (801) and an external electronic device (804) through a server (808) connected to a second network (899). Each of the external electronic devices (802, or 804) may be the same or a different type of device as the electronic device (801). According to one embodiment, all or part of the operations performed on the electronic device (801) may be performed on one or more of the external electronic devices (802, 804, or 808). For example, if the electronic device (801) needs to perform a function or service automatically or in response to a request from a user or another device, the electronic device (801) may request one or more external electronic devices to perform at least part of the function or service instead of performing the function or service itself or additionally. One or more external electronic devices that receive the above request may execute at least part of the requested function or service, or additional function or service related to the request, and transmit the result of the execution to the electronic device (801). The electronic device (801) may provide the result as is or additionally processed as at least part of the response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device (801) may provide ultra-low latency services using, for example, distributed computing or mobile edge computing. In one embodiment, the external electronic device (804) may include an Internet of Things (IoT) device. The server (808) may be an intelligent server using machine learning and / or neural networks. According to one embodiment, the external electronic device (804) or the server (808) may be included within a second network (899).The electronic device (801) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0133] The technical problems to be solved in this disclosure are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which this disclosure pertains.

[0134] As described above, an electronic device (e.g., electronic device (101)) may include at least one processor (e.g., at least one processor (110)) comprising a processing circuit; and a memory (e.g., memory (120)) comprising one or more storage media for storing instructions. The instructions may cause the electronic device to perform the noise reduction of the first image data, when executed individually or collectively by the at least one processor, based on acquiring the first image data; identifying a second image data on which the noise reduction has been performed before performing the noise reduction of the first image data based on acquiring the first image data; acquiring a third image data by blending the first image data and the second image data; identifying weight data for spatial noise reduction of the first image data based on the third image data; and performing the spatial noise reduction by applying the weight data to the first image data.

[0135] For example, the weight data may be a first weight data. The instructions, when executed individually or collectively by the at least one processor, may cause the electronic device to: identify a second weight data for temporal noise reduction of the first image data based on comparing the first image data with the second image data to perform moving object detection; acquire the third image data based on performing the temporal noise reduction by blending the first image data and the second image data according to the second weight data; and identify the first weight data according to the third image data.

[0136] For example, the above temporal noise reduction may be a first temporal noise reduction. The above instructions may cause the electronic device to perform the noise reduction of the first image data based on: acquiring the fourth image data by performing the spatial noise reduction; and performing the second temporal noise reduction by blending the second image data and the fourth image data, when executed individually or collectively by the at least one processor.

[0137] For example, the third weight data used to blend the second image data and the fourth image data may correspond to the second weight data.

[0138] For example, the third weight data used to blend the second image data and the fourth image data may be different from the second weight data.

[0139] For example, the third weight data used to blend the second image data and the fourth image data may be obtained based on the second weight data. When the instructions are executed individually or collectively by the at least one processor, the electronic device may be caused to: obtain difference data regarding the amount of noise by comparing the fourth image data with the first image data; and adjust the third weight data according to the difference data.

[0140] For example, the electronic device may further include a camera (e.g., at least one camera (130)). The first image data may correspond to a first image obtained through the camera. The second image data may correspond to a second image obtained through the camera before the first image is obtained.

[0141] For example, the first noise difference between the first image and the first image data may be smaller than the second noise difference between the second image and the second image data.

[0142] For example, the number of noise reductions performed on the first image data may be smaller than the number of noise reductions performed on the second image data.

[0143] For example, the weight data can be obtained by comparing a pixel of the third image data with at least one pixel of the third image data adjacent to the pixel of the third image data.

[0144] For example, the spatial noise reduction of the first image data may include an operation of correcting the value of a first pixel of the first image data using at least one value of at least one pixel of the third image data adjacent to the second pixel of the third image data. The location of the first pixel and the location of the second pixel may correspond to each other.

[0145] For example, the operation of blending the first image data and the second image data may be performed with respect to temporal noise reduction of the first image data. The temporal noise reduction of the first image data may include an operation of correcting the value of a first pixel of the first image data using the value of a second pixel of the second image data acquired before the first image data. The position of the first pixel and the position of the second pixel may correspond to each other.

[0146] A non-transient computer-readable storage medium as described above may store one or more programs. The one or more programs may include instructions that, when executed by an electronic device (e.g., electronic device (101)): acquire a first image data; identify a second image data on which noise reduction has been performed before performing noise reduction on the first image data based on acquiring the first image data; acquire a third image data by blending the first image data and the second image data; identify weight data for spatial noise reduction of the first image data based on the third image data; and cause the electronic device to perform the noise reduction of the first image data based on performing the spatial noise reduction by applying the weight data to the first image data.

[0147] For example, the weight data may be a first weight data. The one or more programs may include instructions that, when executed by the electronic device, cause the electronic device to: identify a second weight data for temporal noise reduction of the first image data based on comparing the first image data with the second image data to perform moving object detection; acquire the third image data based on performing the temporal noise reduction by blending the first image data and the second image data according to the second weight data; and identify the first weight data according to the third image data.

[0148] For example, the above temporal noise reduction may be a first temporal noise reduction. The one or more programs may include instructions that cause the electronic device to perform the noise reduction of the first image data based on: acquiring the fourth image data by performing the spatial noise reduction; and performing the second temporal noise reduction by blending the second image data and the fourth image data when executed by the electronic device.

[0149] For example, the electronic device may include a camera (e.g., at least one camera (130)). The first image data may correspond to a first image obtained through the camera. The second image data may correspond to a second image obtained through the camera before the first image is obtained.

[0150] For example, the first noise difference between the first image and the first image data may be smaller than the second noise difference between the second image and the second image data.

[0151] For example, the number of noise reductions performed on the first image data may be smaller than the number of noise reductions performed on the second image data.

[0152] For example, the weight data can be obtained by comparing a pixel of the third image data with at least one pixel of the third image data adjacent to the pixel of the third image data.

[0153] The method described above may be performed by an electronic device (e.g., electronic device (101)). The method may include: an operation of acquiring first image data; an operation of identifying second image data on which noise reduction has been performed before performing noise reduction on the first image data based on acquiring the first image data; an operation of acquiring third image data by blending the first image data and the second image data; an operation of identifying weight data for spatial noise reduction of the first image data based on the third image data; and an operation of performing noise reduction on the first image data based on performing spatial noise reduction by applying the weight data to the first image data.

[0154] The effects obtainable from the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present disclosure belongs.

[0155] The electronic device according to the various embodiments disclosed in this document may be of various forms. The electronic device may include, for example, a portable communication device (e.g., a smartphone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a consumer electronics device. The electronic device according to the embodiments of this document is not limited to the devices described above.

[0156] The various embodiments of this document and the terms used therein are not intended to limit the technical features described in this document to specific embodiments, and should be understood to include various modifications, equivalents, or substitutions of said embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of said items unless the relevant context clearly indicates otherwise. In this document, phrases such as "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" may each include any one of the items listed together in the corresponding phrase, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used simply to distinguish said components from other said components and do not limit said components in any other aspect (e.g., importance or order). Where any (e.g., 1st) component is referred to as "coupled" or "connected" to another (e.g., 2nd) component, with or without the terms "functionally" or "communicationly," it means that said any component may be connected to said other component directly (e.g., via a wire), wirelessly, or through a third component.

[0157] The term “module” as used in the 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, for example. A module may be a component formed integrally, or a minimum unit of said component or a part thereof 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).

[0158] Various embodiments of the present document may be implemented as software (e.g., program (840)) comprising one or more instructions stored in a storage medium (e.g., internal memory (836) or external memory (838)) readable by a machine (e.g., electronic device (801)). For example, a processor (e.g., processor (820)) of the machine (e.g., electronic device (801)) may call at least one of 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 that can be executed by an interpreter. The storage medium readable by the machine may be provided in the form of a non-transitory storage medium. Here, 'non-temporary' simply means that the storage medium is a tangible device and does not contain a signal (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently and cases where it is stored temporarily.

[0159] According to one embodiment, the method according to the various embodiments disclosed herein may be provided by being included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or distributed online (e.g., download or upload) through an application store (e.g., Play Store™) or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created on a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0160] According to various embodiments, each component (e.g., module or program) of the components described above may include a singular or multiple entities, and some of the multiple entities may be separated and placed in other components. According to various embodiments, one or more of the components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Generally or additionally, multiple components (e.g., module or program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as those performed by the corresponding component among the multiple components prior to integration. According to various embodiments, operations performed by the module, program, or other components 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.

Claims

1. In an electronic device, At least one processor including a processing circuit; and Memory that stores instructions and includes one or more storage media, When the above instructions are executed individually or collectively by the at least one processor: Acquire first image data; Based on acquiring the first image data, identify the second image data on which noise reduction has been performed before performing noise reduction on the first image data; A third image data is obtained by blending the first image data and the second image data; Identifying weight data for spatial noise reduction of the first image data according to the third image data; and Based on performing spatial noise reduction by applying the above weight data to the first image data, the noise reduction of the first image data is performed. The above electronic device, causing, Electronic device.

2. In Claim 1, The above weight data is the first weight data, and When the above instructions are executed individually or collectively by the at least one processor: Based on comparing the first image data with the second image data to perform moving object detection, second weight data for temporal noise reduction of the first image data is identified; Based on performing temporal noise reduction by blending the first image data and the second image data according to the second weight data, the third image data is obtained; and To identify the first weight data according to the third image data, The above electronic device, causing, Electronic device.

3. In Claim 2, The above temporal noise reduction is a first temporal noise reduction, and When the above instructions are executed individually or collectively by the at least one processor: Acquire the fourth image data by performing the above spatial noise reduction; Based on performing a second temporal noise reduction by blending the second image data and the fourth image data, the noise reduction of the first image data is performed. The above electronic device, causing, Electronic device.

4. In Claim 3, The third weight data used to blend the second image data and the fourth image data corresponds to the second weight data. Electronic device.

5. In Claim 3, The third weight data used to blend the second image data and the fourth image data is different from the second weight data. Electronic device.

6. In Claim 3, The third weight data used to blend the second image data and the fourth image data is obtained based on the second weight data, and When the above instructions are executed individually or collectively by the at least one processor: By comparing the above-mentioned fourth image data with the above-mentioned first image data, difference data regarding the amount of noise is obtained; and To adjust the third weight data according to the difference data above, The above electronic device, causing, Electronic device.

7. In Claim 1, The above electronic device is, Includes more cameras, The first image data above corresponds to a first image obtained through the camera, and The second image data above corresponds to the second image acquired through the camera before the first image is acquired, Electronic device.

8. In Claim 7, The first noise difference between the first image and the first image data is smaller than the second noise difference between the second image and the second image data. Electronic device.

9. In Claim 7, The number of noise reductions performed on the first image data is smaller than the number of noise reductions performed on the second image data. Electronic device.

10. In Claim 1, The above weight data is obtained by comparing a pixel of the third image data with at least one pixel of the third image data adjacent to the pixel of the third image data. Electronic device.

11. In Claim 1, The spatial noise reduction of the first image data includes an operation of correcting the value of a first pixel of the first image data using at least one value of at least one pixel of the third image data adjacent to the second pixel of the third image data, and The position of the first pixel and the position of the second pixel correspond to each other. Electronic device.

12. In Claim 1, The operation of blending the first image data and the second image data is performed with respect to temporal noise reduction of the first image data, and The temporal noise reduction of the first image data includes an operation of correcting the value of a first pixel of the first image data using the value of a second pixel of the second image data acquired before the first image data, and The position of the first pixel and the position of the second pixel correspond to each other. Electronic device.

13. In a non-transient computer-readable storage medium storing one or more programs, said one or more programs, when executed by an electronic device: Acquire first image data; Based on acquiring the first image data, identify the second image data on which noise reduction has been performed before performing noise reduction on the first image data; A third image data is obtained by blending the first image data and the second image data; Identifying weight data for spatial noise reduction of the first image data according to the third image data; and Based on performing spatial noise reduction by applying the above weight data to the first image data, the noise reduction of the first image data is performed. Instructions including those that cause the above electronic device Non-transient computer-readable storage media.

14. In Claim 13, The above weight data is the first weight data, and When one or more of the above programs are executed by the electronic device: Based on comparing the first image data with the second image data to perform moving object detection, second weight data for temporal noise reduction of the first image data is identified; Based on performing temporal noise reduction by blending the first image data and the second image data according to the second weight data, the third image data is obtained; and To identify the first weight data according to the third image data, Instructions including those that cause the above electronic device Non-transient computer-readable storage media.

15. In Claim 14, The above temporal noise reduction is a first temporal noise reduction, and When one or more of the above programs are executed by the electronic device: Acquire the fourth image data by performing the above spatial noise reduction; Based on performing a second temporal noise reduction by blending the second image data and the fourth image data, the noise reduction of the first image data is performed. Instructions including those that cause the above electronic device Non-transient computer-readable storage media.

Images