Image processing method and device, electronic equipment and storage medium

Abstract

The present disclosure relates to an image processing method and device, electronic equipment and storage medium. The method comprises: obtaining an initial image by shooting; determining initial parameters of each image processing index of the initial image; collecting and generating a plurality of candidate parameters corresponding to each image processing index with each initial parameter as the center; obtaining a plurality of candidate images based on each candidate parameter under the same shooting scene as the initial image; determining a target image from the plurality of candidate images based on the quality parameters of each candidate image, and selecting an image processing parameter corresponding to the target image from the plurality of candidate parameters. The present disclosure selects the image processing parameter based on the plurality of candidate parameters corresponding to each image processing index and the candidate images obtained by the shooting equipment, thereby overcoming the problems of time-consuming and difficulty in achieving the optimal selection of the image processing parameter.

Description

Technical Field

[0001] This disclosure relates to the field of image processing, and more particularly to an image processing method, apparatus, electronic device, and storage medium. Background Technology

[0002] Commercial imaging systems rely on image processing workflows, which typically consist of several image processing modules containing a large number of image processing parameters used to process the unprocessed image formed by the sensor into an image with optimal visual quality.

[0003] Traditional methods typically require experienced engineers to spend a significant amount of time individually debugging image processing parameters and evaluating the processed images to select the optimal parameters that achieve the best visual effect. This approach not only consumes substantial time resources but also makes it difficult to guarantee that the selected optimal parameters are globally or locally optimal. Summary of the Invention

[0004] This disclosure provides an image processing method, apparatus, electronic device, and storage medium to overcome the problems of time-consuming and difficult-to-optimize image processing parameter selection.

[0005] According to a first aspect of the present disclosure, an image processing method is provided, comprising:

[0006] Capture the initial image;

[0007] Determine the initial parameters for each image processing index of the initial image;

[0008] Based on each of the initial parameters, multiple candidate parameters corresponding to each of the image processing indicators are collected and generated.

[0009] In the same shooting scenario as the initial image, multiple candidate images are obtained by shooting based on each of the candidate parameters;

[0010] Based on the quality parameters of each candidate image, a target image is determined from the candidate images, and image processing parameters corresponding to the target image are selected from the candidate parameters.

[0011] In some embodiments, generating multiple candidate parameters corresponding to each of the image processing metrics by traversing around each of the initial parameters includes:

[0012] Based on the brightness parameters of the initial image and / or the type of the image processing index, determine the acquisition range and acquisition step size of each of the initial parameters;

[0013] Within the acquisition range centered on the initial parameter, multiple candidate parameters corresponding to each initial parameter are acquired and generated according to the acquisition step size.

[0014] In some embodiments, the initial image includes dynamic objects and static objects;

[0015] The step of capturing multiple candidate images based on various candidate parameters in the same shooting scene as the initial image includes:

[0016] Select dynamic processing parameters from multiple candidate parameters corresponding to the image processing indicators of the dynamic object;

[0017] In the same shooting scenario as the initial image, multiple candidate images are captured based on multiple candidate parameters corresponding to the dynamic processing parameters and the image processing indicators of the static object.

[0018] In some embodiments, selecting dynamic processing parameters from a plurality of candidate parameters corresponding to the image processing metrics of the dynamic object includes:

[0019] Based on multiple candidate parameters corresponding to the image processing indicators of the dynamic object, multiple images to be processed are captured.

[0020] The image to be processed is cropped to obtain a cropped image containing the dynamic object;

[0021] Based on the quality parameters of each of the cropped images, a preferred image is determined from the plurality of cropped images;

[0022] The candidate parameters corresponding to the image processing index of the preferred image are selected from the candidate parameters corresponding to the image processing index of the dynamic object and determined as the dynamic processing parameters.

[0023] In some embodiments, selecting the image processing parameters corresponding to the target image from a plurality of candidate parameters includes:

[0024] Select the static processing parameters corresponding to the target image from multiple candidate parameters corresponding to the image processing indicators of the static object;

[0025] The image processing parameters are obtained by combining the dynamic processing parameters and the static processing parameters.

[0026] In some embodiments, the image processing metrics for the static object include at least: multi-channel spatial noise filtering;

[0027] The image processing metrics for the dynamic object include at least one of the following: temporal filtering, spatial and frequency filtering, and adaptive Bayer filtering.

[0028] In some embodiments, the quality parameters include: sharpness parameters and noise parameters;

[0029] Determining a target image from a plurality of candidate images based on the quality parameters of each candidate image includes:

[0030] Determine the sharpness parameters of each candidate image, and plot a sharpness curve based on the sharpness parameters of each candidate image;

[0031] The noise parameters of each candidate image are determined, and a noise curve is plotted based on the noise parameters of each candidate image.

[0032] The target image is determined from a plurality of candidate images based on the intersection of the sharpness curve and the noise curve.

[0033] In some embodiments, the sharpness parameter is obtained by the following method:

[0034] Singular value decomposition is performed on the candidate image to obtain the singular values ​​of the candidate image;

[0035] The reciprocal of the singular value is used as the sharpness parameter of the candidate image.

[0036] In some embodiments, the noise parameters are obtained by the following method:

[0037] When the candidate image contains dynamic objects, the structural similarity index between each candidate image and the initial image is calculated, and the structural similarity index is used as the noise parameter of the candidate image.

[0038] When the candidate image contains a static object, the pixel value of the candidate image is obtained and the pixel value is used as the noise parameter of the candidate image.

[0039] In some embodiments, the image processing method further includes: debugging the image processing function of the device to be debugged based on the image processing parameters.

[0040] According to a second aspect of the present disclosure, an image processing apparatus is provided, comprising:

[0041] The acquisition module is configured to capture the initial image.

[0042] The first determining module is configured to determine the initial parameters of each image processing index of the initial image;

[0043] The acquisition module is configured to acquire and generate multiple candidate parameters corresponding to each of the image processing indicators, centered on each of the initial parameters.

[0044] The shooting module is configured to capture multiple candidate images based on each of the candidate parameters in the same shooting scene as the initial image.

[0045] The second determining module is configured to determine a target image from a plurality of candidate images based on the quality parameters of each candidate image, and to select image processing parameters corresponding to the target image from a plurality of candidate parameters.

[0046] In some embodiments, the acquisition module is further configured to:

[0047] Based on the brightness parameters of the initial image and / or the type of the image processing index, determine the acquisition range and acquisition step size of each of the initial parameters;

[0048] Within the acquisition range centered on the initial parameter, multiple candidate parameters corresponding to each initial parameter are acquired and generated according to the acquisition step size.

[0049] In some embodiments, the initial image includes dynamic and static objects; the capturing module is further configured to:

[0050] Select dynamic processing parameters from multiple candidate parameters corresponding to the image processing indicators of the dynamic object;

[0051] In the same shooting scenario as the initial image, multiple candidate images are captured based on multiple candidate parameters corresponding to the dynamic processing parameters and the image processing indicators of the static object.

[0052] In some embodiments, the shooting module is further configured to:

[0053] Based on multiple candidate parameters corresponding to the image processing indicators of the dynamic object, multiple images to be processed are captured.

[0054] The image to be processed is cropped to obtain a cropped image containing the dynamic object;

[0055] Based on the quality parameters of each of the cropped images, a preferred image is determined from the plurality of cropped images;

[0056] The candidate parameters corresponding to the image processing index of the preferred image are selected from the candidate parameters corresponding to the image processing index of the dynamic object and determined as the dynamic processing parameters.

[0057] In some embodiments, the shooting module is further configured to:

[0058] Select the static processing parameters corresponding to the target image from multiple candidate parameters corresponding to the image processing indicators of the static object;

[0059] The image processing parameters are obtained by combining the dynamic processing parameters and the static processing parameters.

[0060] In some embodiments, the second determining module is further configured to:

[0061] Determine the sharpness parameters of each candidate image, and plot a sharpness curve based on the sharpness parameters of each candidate image;

[0062] The noise parameters of each candidate image are determined, and a noise curve is plotted based on the noise parameters of each candidate image.

[0063] The target image is determined from a plurality of candidate images based on the intersection of the sharpness curve and the noise curve.

[0064] In some embodiments, the second determining module is further configured to:

[0065] Singular value decomposition is performed on the candidate image to obtain the singular values ​​of the candidate image;

[0066] The reciprocal of the singular value is used as the sharpness parameter of the candidate image.

[0067] In some embodiments, the second determining module is further configured to:

[0068] When the candidate image contains dynamic objects, the structural similarity index between each candidate image and the initial image is calculated, and the structural similarity index is used as the noise parameter of the candidate image.

[0069] When the candidate image contains a static object, the pixel value of the candidate image is obtained and the pixel value is used as the noise parameter of the candidate image.

[0070] According to a third aspect of the present disclosure, an electronic device is provided, comprising:

[0071] processor;

[0072] Memory configured to store processor-executable instructions;

[0073] The processor is configured to execute the image processing method described in the first aspect when it invokes executable instructions in memory.

[0074] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the image processing method described in the first aspect.

[0075] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0076] The image processing method disclosed herein involves capturing an initial image, determining initial parameters for various image processing metrics of the initial image, and generating multiple candidate parameters corresponding to each initial parameter. Under the same shooting scene as the initial image, multiple candidate images are captured based on the candidate parameters. Based on the quality parameters of each candidate image, a target image is determined from the multiple candidate images, and the image processing parameters corresponding to the target image are selected from the multiple candidate parameters. In other words, this disclosure uses a large number of candidate parameters to capture images and obtain a large number of candidate images for selecting image processing parameters. This avoids the time-consuming problem and difficulty in achieving globally optimal parameter selection when directly generating candidate images from candidate parameters without considering interference from the shooting device itself.

[0077] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0078] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0079] Figure 1 This is a flowchart illustrating an image processing method according to an exemplary embodiment. Figure 1 .

[0080] Figure 2 This is a flowchart illustrating an image processing method according to an exemplary embodiment. Figure 2 .

[0081] Figure 3 This is a flowchart illustrating an image processing method according to an exemplary embodiment. Figure 3 .

[0082] Figure 4 This is a flowchart illustrating an image processing method according to an exemplary embodiment. Figure 4 .

[0083] Figure 5 This is a flowchart illustrating an image processing method according to an exemplary embodiment. Figure 5 .

[0084] Figure 6 This is a flowchart illustrating an image processing method according to an exemplary embodiment. Figure 6 .

[0085] Figure 7 This is a schematic diagram of the structure of an image processing apparatus according to an exemplary embodiment.

[0086] Figure 8 This is a schematic diagram of the structure of an electronic device according to an exemplary embodiment. Detailed Implementation

[0087] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0088] In this specification, unless otherwise expressly stated, the terms "first" and "second" are used only to describe and distinguish the constituent elements, and should not be construed as indicating order. Unless otherwise expressly stated, the terms "connection," "fixing," etc., should be understood in a broad sense, including but not limited to "connecting" or "fixing" directly, indirectly, or detachably.

[0089] Figure 1 This is a flowchart of an image processing method according to an exemplary embodiment. Figure 1 ,like Figure 1 As shown, the method mainly includes the following steps:

[0090] In step 101, an initial image is captured.

[0091] In this embodiment, the executing entity of the image processing method can be the device under test. The device under test starts capturing images by executing a pre-stored debugging program, thus completing the debugging of the image processing function. Alternatively, the executing entity of the image processing method can be a professional debugging device (such as a PC). The debugging device executes a debugging program to complete the debugging of the image processing function in the device under test (mobile phone). In other words, the entire process requires the debugging device and the device under test to cooperate with each other.

[0092] In step 102, the initial parameters of each image metric of the initial image are determined.

[0093] The device to be debugged can be a device with an imaging system, including both a hardware imaging module and a software image processing module. Examples include mobile phones, tablets, computers, cameras, and smartwatches. Before leaving the factory, the device needs to have numerous image processing parameters within its image processing module adjusted and selected to optimize image capture performance. Therefore, this disclosure obtains the initial image captured by the device to be debugged, thereby acquiring the initial parameters of the initial image regarding various image processing indicators. Image processing indicators can include metrics related to various image processing methods that improve the visual effect of the image, such as noise reduction, filtering, exposure, and brightness. For example, regarding noise reduction, the image processing indicator can be any one or more of mean filtering, Gaussian filtering, median filtering, etc., and the corresponding initial parameters are any one or more of the mean filtering parameters, Gaussian filtering parameters, median filtering parameters, etc.

[0094] In step 103, multiple candidate parameters corresponding to each image processing index are collected and generated, centered on each initial parameter.

[0095] In this embodiment, the initial images captured all have basic, unadjusted initial parameters. Based on these initial parameters, this disclosure collects multiple candidate parameters within a range of real numbers larger and / or smaller than the initial parameters. For example, if the initial parameter of index A is 8.7, multiple candidate parameters can be selected within a range greater than 8.7, or multiple candidate parameters can be selected within a range less than 8.7. Thus, the candidate parameters for index A can be exemplarily {…,8.1,8.3,8.5,8.9,9.1,…}. That is, each image processing index corresponds to one initial parameter, and one initial parameter corresponds to multiple candidate parameters. The image processing parameters that enable the device under test to capture images to achieve the best results are selected from the candidate parameters.

[0096] In step 104, multiple candidate images are captured based on various candidate parameters in the same shooting scene as the initial image.

[0097] In this embodiment, multiple candidate parameters generated by the acquisition are imported into the device to be debugged, so that the device to be debugged can capture multiple candidate images in the same shooting scene as the initial image. At this time, the candidate images and the initial images are in the same shooting scene, that is, the shooting content, shooting direction and shooting position of the candidate images and the initial images are the same. Therefore, the multiple candidate images are also the same in shooting content, shooting direction and shooting position. Only in this way can the multiple candidate images obtained be used as the basis for selecting the target image.

[0098] Furthermore, each image processing indicator corresponds to a candidate parameter set, and multiple candidate parameters corresponding to each image processing indicator are located in the candidate parameter set corresponding to each image processing indicator; step 103 includes: combining the candidate parameters selected from the candidate parameter sets corresponding to each image processing indicator to obtain a candidate parameter group; and acquiring multiple candidate images captured by the device under test based on multiple candidate parameter groups. That is, when multiple candidate images are obtained, this disclosure can import different candidate parameters of each image processing indicator separately for shooting, or it can combine different candidate parameters of each image processing indicator into a candidate parameter group for importing and shooting.

[0099] For example, image processing metrics include A, B, and C. Candidate parameters for metric A include A1, A2, and A3; candidate parameters for metric B include B1 and B2; and candidate parameters for metric C include C1, C2, C3, and C4. These nine candidate parameters can be imported to obtain nine candidate images. The optimal image processing parameters (A, B, and C) are then selected from these. Alternatively, combinations such as (A1, B1, C1), (A2, B1, C1), (A3, B1, C1), and so on, can be used to obtain 24 candidate parameter groups to expedite the selection and debugging process. This method also considers the influence between parameters of different metric types.

[0100] In step 105, based on the quality parameters of each candidate image, the target image is determined from multiple candidate images, and the image processing parameters corresponding to the target image are selected from multiple candidate parameters.

[0101] In this embodiment, a quality evaluation is performed on each candidate image to obtain quality parameters for each candidate image. The dimensions of the quality evaluation can be determined based on the application scenario of the device under test, the characteristics of the imaging system of the device under test, the user's requirements for the visual effect of the image, etc. For example, quality parameters may include sharpness, brightness, distortion, noise, etc. When sharpness is used as a quality parameter, the higher the sharpness of the candidate image, the more likely it is to be the target image; when distortion is used as a quality parameter, the lower the distortion of the candidate image, the more likely it is to be the target image; when noise is used as a quality parameter, the lower the noise of the candidate image, the more likely it is to be the target image.

[0102] The candidate parameters corresponding to the image with the best quality and visual effect are selected from the candidate images as image processing parameters. Thus, the image processing method disclosed herein takes into account the influence of the device under test itself on the selection of image processing parameters. It allows the device under test to capture different candidate images under different candidate parameter conditions as the basis for selection, rather than directly processing the initial image based on the acquired candidate parameters and then selecting image processing parameters based on the processed image. This improves the accuracy of image processing parameter selection and increases the probability that the final selection result will reach the global optimum.

[0103] It should be noted that the image processing method disclosed herein is applied to an electronic device that has completed the debugging process, or to an electronic device that has performed the above steps to select the image processing parameters for the device to be debugged.

[0104] In some embodiments, step 103 includes: determining the acquisition range and acquisition step size of each initial parameter based on the brightness parameter and / or the type of image processing index of the initial image; and acquiring multiple candidate parameters corresponding to each initial parameter within the acquisition range centered on the initial parameter according to the acquisition step size.

[0105] Specifically, the acquisition range and step size of candidate parameters differ depending on the brightness of the initial image; similarly, the acquisition range and step size also differ depending on the type of image processing metric. A larger brightness parameter results in a brighter initial image, but a smaller acquisition range and step size; conversely, a smaller brightness parameter results in a darker initial image, but a larger acquisition range and step size. For example, if the initial parameter of a certain image processing metric is 10, the acquisition range is 0-20, and the acquisition step size is 2, then the obtained candidate parameters include: 0, 2, 4, 6, 8, 10, 12, 14, 16, and 18.

[0106] In some embodiments, such as Figure 2 As shown, when the initial image contains both dynamic and static objects, step 104 includes:

[0107] In step 1041, dynamic processing parameters are selected from multiple candidate parameters corresponding to the image processing indicators of the dynamic object.

[0108] In step 1042, under the same shooting scene as the initial image, multiple candidate images are captured based on multiple candidate parameters corresponding to the dynamic processing parameters and the image processing indicators of the static object.

[0109] In this embodiment, dynamic objects refer to moving objects in the shooting scene, which are objects with motion blur in the initial image, such as moving cars, runners, and trees swaying in the wind. Similarly, static objects refer to stationary objects in the shooting scene, such as sitting people and bottles placed on the ground. When the initial image contains both dynamic and static objects, this embodiment first selects the image processing parameters corresponding to the dynamic objects (dynamic processing parameters), and then selects the image processing parameters corresponding to the static objects (static processing parameters) based on the dynamic processing parameters. It is understood that, under the same environment, dynamic objects often have worse image capture effects than static objects due to instability and difficulty in tracking and capturing them. Moreover, processing the image area of ​​dynamic objects often affects the display effect of the image area of ​​static objects. Therefore, selecting the dynamic processing parameters first and then selecting the static processing parameters based on the dynamic processing parameters can effectively improve the efficiency of parameter selection and tuning and increase the possibility of selecting the globally optimal result.

[0110] Specifically, firstly, dynamic processing parameters are selected from multiple candidate parameters corresponding to dynamic objects. These dynamic processing parameters can optimize the visual effect of the image region containing dynamic objects. Then, the dynamic processing parameters and multiple candidate parameters corresponding to the image processing indicators of static objects are combined and imported into the device to be debugged to capture multiple candidate images. The visual effect of the image region of dynamic objects in these candidate images has been optimized. Subsequently, it is only necessary to select the static processing parameters that can optimize the visual effect of the image region of static objects in the candidate images.

[0111] Furthermore, such as Figure 2 As shown, step 105 involves selecting image processing parameters corresponding to the target image from multiple candidate parameters, including:

[0112] In step 1054, static processing parameters corresponding to the target image are selected from multiple candidate parameters corresponding to the image processing indicators of the static object.

[0113] In step 1055, the dynamic processing parameters and the static processing parameters are combined to obtain the image processing parameters.

[0114] In this embodiment, when the initial image contains both static and dynamic objects, after importing multiple candidate parameters corresponding to the dynamic processing parameters and the image processing indicators of the static objects into the device to be debugged to obtain candidate images (at this time, the visual effect of the image region of the dynamic object in the candidate image has reached the best), the target image is determined from the candidate images based on the quality parameters of the candidate images. At this time, the quality evaluation of the candidate images is mainly aimed at the image region of the static objects in the candidate images, while the determined target image is the one in which the image regions of both the static objects and the dynamic objects have the best quality among all candidate images.

[0115] Further, step 1041 includes: capturing multiple images to be processed based on multiple candidate parameters corresponding to the image processing indicators of the dynamic object; cropping the images to be processed to obtain cropped images containing the dynamic object; determining a preferred image from the multiple cropped images based on the quality parameters of each cropped image; and determining the multiple candidate parameters corresponding to the image processing indicators of the preferred image selected from the multiple candidate parameters corresponding to the image processing indicators of the dynamic object as dynamic processing parameters.

[0116] Specifically, dynamic objects and static objects correspond to different image processing indicators. When selecting dynamic processing parameters, multiple candidate parameters corresponding to the image processing indicators of the dynamic objects are imported into the device to be debugged. The device to be debugged captures multiple images to be processed under the same shooting environment as the initial image. Then, in order to eliminate the interference of static objects, the images to be processed are cropped to obtain cropped images containing dynamic objects. The quality of the cropped images containing dynamic objects is evaluated, and the best image with the best quality and visual effect is selected. The candidate parameters corresponding to this image are the dynamic processing parameters, which are the image processing parameters that enable the image effect of dynamic objects to reach the optimal level.

[0117] Of course, the cropping step in the above process is not necessarily required. Alternatively, the image to be processed can be skipped, and only the image regions of dynamic objects in the image to be processed can be evaluated during the quality assessment process to obtain quality parameters. The specific processing flow can be selected and adjusted according to processing needs and ease of processing.

[0118] In addition, when the initial image contains only dynamic objects (or static objects), the initial parameters corresponding to the image processing indicators of the dynamic objects (or static objects) are directly determined. Based on the initial parameters, multiple candidate parameters corresponding to the image processing indicators of the dynamic objects (or static objects) are obtained. Based on the candidate parameters, candidate images are obtained. Based on the candidate images, dynamic processing parameters (or static processing parameters) are obtained. At this time, the dynamic processing parameters or static processing parameters are the image processing parameters.

[0119] In one embodiment, the image processing metrics for static objects include at least: multi-channel spatial noise filtering; the image processing metrics for dynamic objects include at least one of the following: time-domain filtering, spatial-domain and frequency-domain filtering, and adaptive Bayer filtering.

[0120] In some embodiments, the quality parameters include: sharpness parameters and noise parameters; such as Figure 2 As shown, in step 105, the target image is determined from multiple candidate images based on the quality parameters of each candidate image, including:

[0121] In step 1051, the sharpness parameters of each candidate image are determined, and a sharpness curve is plotted based on the sharpness parameters of each candidate image.

[0122] In step 1052, the noise parameters of each candidate image are determined, and a noise curve is plotted based on the noise parameters of each candidate image.

[0123] In step 1053, the target image is determined from multiple candidate images based on the intersection of the sharpness curve and the noise curve.

[0124] In this embodiment, the quality of the candidate image is measured from two aspects: sharpness and noise. Sharpness and noise are two contradictory image processing metrics; that is, higher image sharpness corresponds to greater image noise, and lower image sharpness corresponds to less image noise. Therefore, this disclosure plots sharpness curves and noise curves separately. The horizontal axis of the two curves represents the candidate image's label, and the vertical axis represents the sharpness parameter value or noise parameter value. The intersection of the sharpness curve and the noise curve is the balance point between sharpness and noise; the candidate image corresponding to this point is the target image.

[0125] Furthermore, the image processing method also includes: performing singular value decomposition on the candidate image to obtain the singular values ​​of the candidate image; and using the reciprocal of the singular values ​​as the sharpness parameter of the candidate image.

[0126] Specifically, an RGB image is composed of multiple pixel units, which can be regarded as a three-dimensional matrix during data processing. Singular Value Decomposition (SVD) can decompose the three-dimensional matrix of the image to obtain singular values. These singular values ​​can represent most of the information of the image. The smaller the reciprocal of the singular value, the better the image clarity.

[0127] Furthermore, the image processing method also includes: when the candidate image contains dynamic objects, calculating the structural similarity index between each candidate image and the initial image respectively, and using the structural similarity index as the noise parameter of the candidate image; when the candidate image contains static objects, obtaining the pixel value of the candidate image, and using the pixel value as the noise parameter of the candidate image.

[0128] Specifically, the structural similarity index (SSIM) is a metric used to quantify the structural similarity between two images. SSIM mimics the human visual system (HVS) to implement theories related to structural similarity, being sensitive to the perception of local structural changes in an image. SSIM quantifies image attributes such as brightness, contrast, and structure, using the mean to estimate brightness, variance to estimate contrast, and covariance to estimate the degree of structural similarity. A smaller SSIM value indicates less noise. Similarly, smaller pixel values ​​in an image indicate less noise.

[0129] Furthermore, when obtaining the quality parameters of candidate images that contain both static and dynamic objects, they are typically cropped to contain only static (or dynamic) objects before calculating the quality parameters of the cropped image. However, when the visual effect of the image region containing the dynamic (or static) object in the candidate image is already optimal, cropping is not necessary when calculating the quality parameters of the candidate image.

[0130] In some embodiments, the image processing method further includes: importing image processing parameters into the device to be debugged to complete the debugging of the image processing function of the device to be debugged.

[0131] Figure 3 This is an exemplary embodiment illustrating an image processing method. The method uses a mobile phone as the device to be debugged and a PC as the electronic device to complete the debugging process. The method includes the following steps:

[0132] In step 301, the initial image captured by the mobile phone is obtained;

[0133] In step 302, it is determined whether the sharpness and noise of the initial image meet the standards;

[0134] The process ends when the sharpness and noise of the initial image meet the standards. If the sharpness and noise of the initial image do not meet the standards, proceed to steps 303-308:

[0135] In step 303, the initial parameters of each image processing index of the initial image are determined, and multiple candidate parameters corresponding to each image processing index are collected and generated with each initial parameter as the center.

[0136] In step 304, multiple candidate images are obtained by the mobile phone in the same shooting scene as the initial image, based on each candidate parameter;

[0137] In step 305, the target image is determined from multiple candidate images based on the quality parameters of each candidate image;

[0138] In step 306, it is determined whether the target image represents the optimal visual effect;

[0139] If the target image has the best visual effect, proceed to step 307; if the target image does not have the best visual effect, proceed to step 308.

[0140] In step 307, the image processing parameters corresponding to the target image are output;

[0141] In step 308, the target image is reselected.

[0142] In this embodiment, the above image processing parameter selection process is completed through a computer, the image processing parameters are obtained, and the debugged image processing parameters are imported into the mobile phone to complete the debugging of the mobile phone's image processing function.

[0143] When the initial image captured by the mobile phone contains moving objects, the specific process for determining the target image is as follows: Figure 4 As shown, it includes:

[0144] In step 401, multiple candidate images are obtained by the mobile phone in the same shooting scene as the initial image, based on various candidate parameters;

[0145] In step 402, multiple candidate images are cropped to obtain multiple cropped images containing dynamic objects;

[0146] In step 403, sharpness and noise are calculated for multiple cropped images, where the structural similarity index mssim is used as noise and the reciprocal of the singular value 1 / SVD is used as sharpness;

[0147] In step 404, a target image is selected based on the sharpness and noise of multiple cropped images.

[0148] This embodiment is for the target image selected for the initial image containing dynamic objects. The process identical to steps 302-303 and steps 306-308 is omitted in this embodiment. For specific implementation details, please refer to the foregoing embodiment, which will not be repeated here.

[0149] When the initial image captured by the mobile phone contains static objects, the specific process for determining the target image is as follows: Figure 5 As shown, it includes:

[0150] In step 501, multiple candidate images are obtained by the mobile phone in the same shooting scene as the initial image, based on various candidate parameters;

[0151] In step 502, sharpness and noise are calculated for multiple candidate images, where the image pixel value FV is used as noise and the reciprocal of the image singular value 1 / SVD is used as sharpness.

[0152] In step 503, a target image is selected based on the sharpness and noise of multiple cropped images.

[0153] This embodiment is for the target image selected for the initial image containing static objects. The process identical to steps 302-303 and steps 306-308 is omitted in this embodiment. For specific implementation details, please refer to the foregoing embodiment, which will not be repeated here.

[0154] Figure 6 This is the specific process of determining the target image based on sharpness and noise, including the following steps:

[0155] In step 601, multiple candidate images that need to be calculated are obtained;

[0156] In step 602, the sharpness of the candidate images is calculated;

[0157] In step 603, the noise of the candidate image is calculated;

[0158] In step 604, the sharpness and noise metrics are normalized;

[0159] In step 605, a sharpness variation curve is plotted;

[0160] In step 606, the noise variation curve is plotted;

[0161] In step 607, the intersection of the two curves is the balance point between noise and sharpness, and the corresponding horizontal axis is the target image.

[0162] This embodiment describes the process by which a computer determines a target image based on the sharpness and noise of multiple candidate images acquired by a mobile phone.

[0163] Figure 7 This is an image processing apparatus illustrated according to an exemplary embodiment, such as... Figure 7 As shown, the image processing apparatus includes:

[0164] The acquisition module 701 is configured to capture an initial image; the first determination module 702 is configured to determine the initial parameters of each image processing index of the initial image.

[0165] The acquisition module 703 is configured to acquire and generate multiple candidate parameters corresponding to each of the image processing indicators, centered on each of the initial parameters.

[0166] The shooting module 704 is configured to capture multiple candidate images based on each of the candidate parameters in the same shooting scene as the initial image.

[0167] The second determining module 705 is configured to determine a target image from a plurality of candidate images based on the quality parameters of each candidate image, and to select image processing parameters corresponding to the target image from a plurality of candidate parameters.

[0168] In some embodiments, the acquisition module 703 is further configured to:

[0169] Based on the brightness parameters of the initial image and / or the type of the image processing index, determine the acquisition range and acquisition step size of each of the initial parameters;

[0170] Within the acquisition range centered on the initial parameter, multiple candidate parameters corresponding to each initial parameter are acquired and generated according to the acquisition step size.

[0171] In some embodiments, the initial image includes dynamic objects and static objects; the capturing module 704 is further configured to:

[0172] Select dynamic processing parameters from multiple candidate parameters corresponding to the image processing indicators of the dynamic object;

[0173] In the same shooting scenario as the initial image, multiple candidate images are captured based on multiple candidate parameters corresponding to the dynamic processing parameters and the image processing indicators of the static object.

[0174] In some embodiments, the shooting module 704 is further configured to:

[0175] Based on multiple candidate parameters corresponding to the image processing indicators of the dynamic object, multiple images to be processed are captured.

[0176] The image to be processed is cropped to obtain a cropped image containing the dynamic object;

[0177] Based on the quality parameters of each of the cropped images, a preferred image is determined from the plurality of cropped images;

[0178] The candidate parameters corresponding to the image processing index of the preferred image are selected from the candidate parameters corresponding to the image processing index of the dynamic object and determined as the dynamic processing parameters.

[0179] In some embodiments, the shooting module 704 is further configured to:

[0180] Select the static processing parameters corresponding to the target image from multiple candidate parameters corresponding to the image processing indicators of the static object;

[0181] The image processing parameters are obtained by combining the dynamic processing parameters and the static processing parameters.

[0182] In some embodiments, the second determining module 705 is further configured to:

[0183] Determine the sharpness parameters of each candidate image, and plot a sharpness curve based on the sharpness parameters of each candidate image;

[0184] The noise parameters of each candidate image are determined, and a noise curve is plotted based on the noise parameters of each candidate image.

[0185] The target image is determined from a plurality of candidate images based on the intersection of the sharpness curve and the noise curve.

[0186] In some embodiments, the second determining module 705 is further configured to:

[0187] Singular value decomposition is performed on the candidate image to obtain the singular values ​​of the candidate image;

[0188] The reciprocal of the singular value is used as the sharpness parameter of the candidate image.

[0189] In some embodiments, the second determining module is further configured to:

[0190] When the candidate image contains dynamic objects, the structural similarity index between each candidate image and the initial image is calculated, and the structural similarity index is used as the noise parameter of the candidate image.

[0191] When the candidate image contains a static object, the pixel value of the candidate image is obtained and the pixel value is used as the noise parameter of the candidate image.

[0192] like Figure 8 As shown, this disclosure also provides an electronic device 800, including:

[0193] Memory 804 is used to store processor-executable instructions;

[0194] Processor 820 is connected to memory 804;

[0195] The processor 820 is configured to execute the image processing method provided by any of the aforementioned technical solutions.

[0196] A block diagram of an electronic device 800 is shown according to an exemplary embodiment. For example, the electronic device 800 may be a smartphone, tablet computer, laptop computer, and portable learning machine, etc.

[0197] refer to Figure 8The electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 818.

[0198] Processing component 802 typically controls the overall operation of electronic device 400, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0199] Memory 804 is configured to store various types of data to support the operation of electronic device 800. Examples of this data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0200] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0201] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0202] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 818. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0203] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0204] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 can detect the on / off state of electronic device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0205] Communication component 818 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 818 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 818 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0206] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0207] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of an electronic device 800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0208] This application provides a non-transitory computer-readable storage medium that, when the instructions in the storage medium are executed by a computer's processor, enables the computer to perform the image processing method described in one or more of the foregoing technical solutions.

[0209] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0210] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. An image processing method, characterized in that, include: The initial image was captured; Determine the initial parameters for each image processing index of the initial image; Based on each of the initial parameters, multiple candidate parameters corresponding to each of the image processing indicators are collected and generated. In the same shooting scenario as the initial image, multiple candidate images are obtained by shooting based on each of the candidate parameters; Based on the quality parameters of each candidate image, a target image is determined from the multiple candidate images, and image processing parameters corresponding to the target image are selected from the multiple candidate parameters; The initial image includes both dynamic and static objects; the process of capturing multiple candidate images based on various candidate parameters in the same shooting scene as the initial image includes: Based on multiple candidate parameters corresponding to the image processing indicators of the dynamic object, multiple images to be processed are captured. The image to be processed is cropped to obtain a cropped image containing the dynamic object; Based on the quality parameters of each of the cropped images, a preferred image is determined from the plurality of cropped images; The candidate parameters corresponding to the image processing index of the preferred image are selected from the candidate parameters corresponding to the image processing index of the dynamic object and determined as the dynamic processing parameters. In the same shooting scenario as the initial image, multiple candidate images are captured based on multiple candidate parameters corresponding to the dynamic processing parameters and the image processing indicators of the static object.

2. The image processing method according to claim 1, characterized in that, The step of collecting and generating multiple candidate parameters corresponding to each of the initial parameters as centers includes: Based on the brightness parameters of the initial image and / or the type of the image processing index, determine the acquisition range and acquisition step size of each of the initial parameters; Within the acquisition range centered on the initial parameter, multiple candidate parameters corresponding to each initial parameter are acquired and generated according to the acquisition step size.

3. The image processing method according to claim 1, characterized in that, The step of selecting the image processing parameters corresponding to the target image from the plurality of candidate parameters includes: Select the static processing parameters corresponding to the target image from multiple candidate parameters corresponding to the image processing indicators of the static object; The image processing parameters are obtained by combining the dynamic processing parameters and the static processing parameters.

4. The image processing method according to claim 3, characterized in that, The image processing metrics for the static object include at least: multi-channel spatial noise filtering; The image processing metrics for the dynamic object include at least one of the following: temporal filtering, spatial filtering, and frequency filtering.

5. The image processing method according to claim 1, characterized in that, The quality parameters include: sharpness parameters and noise parameters; Determining a target image from a plurality of candidate images based on the quality parameters of each candidate image includes: Determine the sharpness parameters of each candidate image, and plot a sharpness curve based on the sharpness parameters of each candidate image; The noise parameters of each candidate image are determined, and a noise curve is plotted based on the noise parameters of each candidate image. The target image is determined from a plurality of candidate images based on the intersection of the sharpness curve and the noise curve.

6. The image processing method according to claim 5, characterized in that, The method further includes: Singular value decomposition is performed on the candidate image to obtain the singular values ​​of the candidate image; The reciprocal of the singular value is used as the sharpness parameter of the candidate image.

7. The image processing method according to claim 5, characterized in that, The method further includes: When the candidate image contains dynamic objects, the structural similarity index between each candidate image and the initial image is calculated, and the structural similarity index is used as the noise parameter of the candidate image. When the candidate image contains a static object, the pixel value of the candidate image is obtained and the pixel value is used as the noise parameter of the candidate image.

8. An image processing apparatus, characterized in that, include: The acquisition module is configured to capture the initial image. The first determining module is configured to determine the initial parameters of each image processing index of the initial image; The acquisition module is configured to acquire and generate multiple candidate parameters corresponding to each of the image processing indicators, centered on each of the initial parameters. A shooting module is configured to capture multiple candidate images based on various candidate parameters in the same shooting scene as the initial image; wherein the initial image includes dynamic objects and static objects; capturing multiple candidate images based on various candidate parameters in the same shooting scene as the initial image includes: capturing multiple images to be processed based on multiple candidate parameters corresponding to the image processing indicators of the dynamic objects; cropping the images to be processed to obtain cropped images containing the dynamic objects; determining a preferred image from the multiple cropped images based on the quality parameters of each cropped image; determining multiple candidate parameters corresponding to the image processing indicators of the preferred image selected from the multiple candidate parameters corresponding to the image processing indicators of the dynamic objects as dynamic processing parameters; and capturing multiple candidate images based on the dynamic processing parameters and the multiple candidate parameters corresponding to the image processing indicators of the static objects in the same shooting scene as the initial image. The second determining module is configured to determine a target image from a plurality of candidate images based on the quality parameters of each candidate image, and to select image processing parameters corresponding to the target image from a plurality of candidate parameters.

9. An electronic device, characterized in that, include: processor; Memory configured to store processor-executable instructions; The processor is configured to execute the image processing method as described in any one of claims 1 to 7 when it invokes executable instructions in memory.

10. A non-transitory computer-readable storage medium, wherein when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is able to perform the image processing method according to any one of claims 1 to 7.

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