Image processing method and related device

Abstract

The invention discloses an image processing method and a related device thereof. The method comprises the following steps: acquiring a to-be-processed first image; generating a first brightness mapping curve, and performing global contrast enhancement processing on the first image according to the first brightness mapping curve to obtain a second image; dividing the first image into a plurality of sub-regions, obtaining a second brightness mapping curve based on the plurality of sub-regions, and performing local contrast enhancement processing on the first image according to initial parameters of pixels in each sub-region, the second brightness mapping curve and feature values of the corresponding sub-regions to obtain a third image; a fourth image with enhanced contrast is obtained according to the second image and / or the third image, and at least part of pixels in the fourth image are obtained through superposition processing of corresponding pixels in the second image and the third image. The visual effect of the image and the video is improved by coordinating the level of the overall contrast of the image.

Description

Technical Field

[0001] This application relates to the field of data processing, and particularly to an image processing method and related device thereof. Background Art

[0002] In the information age, the way people obtain information is becoming more and more multimedia-based. Images and videos, with advantages such as intuitiveness, high information content, and strong appeal, have become the core carriers of information dissemination today.

[0003] In the field of digital image processing, contrast enhancement is a commonly used technology for improving the visual effect and clarity of images. Through contrast enhancement, the details of the image can be made more prominent, and the readability and distinguishability of the image can be improved.

[0004] Currently, the visual effect of a single image obtained through local enhancement processing is relatively good. However, when playing a video or dynamic image, obvious changes in different regions in the time domain will occur after local enhancement processing of multiple frames of images, and visual fatigue is likely to occur after long-term viewing.

[0005] In summary, the image contrast enhancement scheme in the prior art has the technical problem of poor integrity. Summary of the Invention

[0006] To solve the above technical problems, this application provides an image processing method and related device thereof, which can improve the visual effect of images and videos by coordinating the levels of the overall contrast of the images.

[0007] According to one aspect of this application, there is provided an image processing method, including:

[0008] Obtaining a first image to be processed;

[0009] Generating different first brightness mapping curves according to the scene and performing global contrast enhancement processing on the first image according to the first brightness mapping curve to obtain a second image;

[0010] Dividing the first image into a plurality of sub-regions, obtaining a second brightness mapping curve based on the plurality of sub-regions, and performing local contrast enhancement processing on the first image according to the initial parameters of the pixels in each sub-region, the second brightness mapping curve, and the eigenvalue of the corresponding sub-region to obtain a third image;

[0011] Obtaining a fourth image with enhanced contrast according to the second image and / or the third image, wherein at least some pixels in the fourth image are obtained by superimposing and processing the corresponding pixels in the second image and the third image.

[0012] Optionally, at least some pixels in the fourth image are obtained by superimposing and processing the second image and the third image, including:

[0013] Pixels in the fourth image with a gray scale lower than the first threshold are replaced with the corresponding pixels in the second image;

[0014] Pixels in the fourth image with a gray scale higher than the second threshold are replaced with the corresponding pixels in the second image;

[0015] Pixels in the fourth image with a gray scale lower than the second threshold and higher than the first threshold are obtained by superimposing the corresponding pixels in the second image and the third image.

[0016] Optionally, it further includes:

[0017] The target parameter of the pixel in the fourth image with a gray scale equal to the first threshold is obtained by linearly or non-linearly transitioning based on the target parameter corresponding to the adjacent gray scale, and / or the target parameter of the pixel in the fourth image with a gray scale equal to the second threshold is obtained by linearly or non-linearly transitioning based on the target parameter corresponding to the adjacent gray scale.

[0018] Optionally, at least some of the pixels in the fourth image being obtained by superimposing the second image and the third image includes:

[0019] Each pixel in the fourth image is obtained by superimposing the corresponding pixels in the second image and the third image.

[0020] Optionally, the step of generating different first brightness mapping curves according to the scene includes:

[0021] Setting at least three control points, where the at least three control points include a high gray scale threshold point, a low gray scale threshold point, and at least one turning point;

[0022] Fitting a Bézier curve according to the at least three control points; and

[0023] Sampling the Bézier curve to obtain the first brightness mapping curve.

[0024] Optionally, the abscissa of the low gray scale threshold point is the first threshold, and the ordinate is the first brightness; and / or the abscissa of the high gray scale threshold point is the second threshold, and the ordinate is the second brightness, where the first brightness is lower than the second brightness.

[0025] Optionally, fitting a Bézier curve according to the at least three control points satisfies the following formula: , , , are three adjacent control points, t is the gray scale of the pixel, is the target parameter corresponding to each pixel in the second image.

[0026] Optionally, the step of performing local contrast enhancement on the first image to obtain a third image further includes:

[0027] Performing histogram statistics on each of several sub-regions and interpolating between the sub-regions to obtain the second brightness mapping curve;

[0028] Obtaining the gray-scale spatial distribution characteristics and gray-scale statistical characteristics of each sub-region;

[0029] Obtaining the target parameters of the corresponding pixels in the third image according to the initial parameters of the pixels in each sub-region, the second brightness mapping curve, the gray-scale spatial distribution characteristics, and the gray-scale statistical characteristics of the corresponding sub-region.

[0030] Optionally, the target parameters of the corresponding pixels in the third image satisfy the following formula: ; Wherein, is the target parameter of the pixel in the third image, is the first empirical constant, y1 is the initial parameter of the pixel in each sub-region, y2 is the adjustment parameter obtained by the corresponding pixel according to the second brightness mapping curve, , a is the second empirical constant, b is the third empirical constant, is the gray-scale spatial distribution characteristic of the corresponding sub-region, is the gray-scale statistical characteristic of the corresponding sub-region.

[0031] Optionally, the brightness of at least some of the pixels in the fourth image obtained by superimposing the second image and the third image satisfies the following formula: ; wherein, is the target parameter of at least some of the pixels in the fourth image, is the target parameter of the pixel in the third image, is the target parameter of the pixel in the second image, is the first empirical constant, , a is the second empirical constant, b is the third empirical constant, is the gray-scale spatial distribution characteristic of the corresponding sub-region, is the gray-scale statistical characteristic of the corresponding sub-region.

[0032] Optionally, it further includes:

[0033] Performing a brightness dimming process on the fourth image;

[0034] and / or performing a high-gray overexposure suppression process on the first image;

[0035] and / or performing a color cast protection process on the fourth image.

[0036] According to another aspect of the present application, there is provided an image processing apparatus, including:

[0037] An acquisition unit that acquires a first image to be processed;

[0038] A global enhancement unit that generates different first brightness mapping curves according to a scene and performs global contrast enhancement processing on the first image according to the first brightness mapping curve to obtain a second image;

[0039] A local enhancement unit that divides the first image into a plurality of sub-regions, obtains a second brightness mapping curve based on the plurality of sub-regions, and performs local contrast enhancement processing on the first image according to the initial parameters of the pixels in each sub-region, the second brightness mapping curve, and the eigenvalue of the corresponding sub-region to obtain a third image; and

[0040] A processing unit that obtains a fourth image with enhanced contrast according to the second image and / or the third image, wherein at least some pixels in the fourth image are obtained by superimposing and processing the corresponding pixels in the second image and the third image.

[0041] According to still another aspect of the present application, there is provided an electronic device, including:

[0042] A display panel;

[0043] A processor;

[0044] A memory for storing processor-executable instructions,

[0045] wherein the processor is configured to execute the image processing method as described above.

[0046] According to still another aspect of the present application, there is provided a computer-readable storage medium, on which a computer program is stored, wherein when the computer program is executed, the image processing method as described above is implemented.

[0047] According to still another aspect of the present application, there is provided a chip, including the image processing apparatus as described above.

[0048] For the image processing method and its related apparatus provided by the present application, at least some pixels adopt a method of combining local contrast adjustment with overall contrast adjustment. It can not only achieve the purpose of anti-"halo, tomography" and texture enhancement through local adaptive contrast adjustment, but also coordinate the overall contrast through global contrast adjustment to achieve contrast enhancement for the corresponding scene or various filter effects. In addition, the purpose of time-domain anti-flicker can also be achieved. Further improving the overall visual effect of the image.

[0049] Furthermore, the image processing method provided by this application also performs overexposure suppression processing for high gray levels, and / or brightness dimming processing, and / or color cast protection processing on the image on the basis of fusing local and global contrast adjustment processing, so as to further improve the visual effect of the image.

[0050] It should be noted that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A flowchart showing an image processing method provided by an embodiment of this application;

[0052] Figure 2 A flowchart showing the process of generating a first brightness mapping curve in the image processing method provided by an embodiment of this application;

[0053] Figure 3 A schematic diagram showing a first brightness mapping curve in the image processing method provided by an embodiment of this application;

[0054] Figure 4 A flowchart showing the process of performing local contrast enhancement processing on a first image in the image processing method provided by an embodiment of this application;

[0055] Figure 5 A flowchart showing another image processing method provided by an embodiment of this application;

[0056] Figure 6 A schematic diagram showing the structure of an image processing apparatus provided by an embodiment of this application;

[0057] Figure 7 A schematic diagram showing the structure of an electronic device provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] To facilitate the understanding of this application, the following will describe this application more comprehensively with reference to the relevant drawings. The preferred embodiments of this application are shown in the drawings. However, this application can be implemented in different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure content of this application more thorough and comprehensive.

[0059] Figure 1 A flowchart showing an image processing method provided by an embodiment of this application. Figure 2 A flowchart showing the process of generating a first brightness mapping curve in the image processing method provided by an embodiment of this application. Figure 3 A schematic diagram showing a first brightness mapping curve in the image processing method provided by an embodiment of this application.Figure 4 The flowchart shows the process of performing local contrast enhancement processing on a first image in an image processing method provided according to an embodiment of the present application.

[0060] As Figure 1 shown, the image processing method includes the following steps:

[0061] Step S100: Obtain a first image to be processed.

[0062] Exemplarily, the above-mentioned first image to be processed can be image data that can be processed by any display device in the related art, and the embodiments of the present disclosure do not limit this here.

[0063] Step S200: Generate different first brightness mapping curves according to the scene and perform global contrast enhancement processing on the first image according to the first brightness mapping curve to obtain a second image.

[0064] Furthermore, the global contrast enhancement processing provided by the present application is implemented by means of a tone mapping configuration method. The scene includes, for example, dark field enhancement, sunlight screen enhancement, etc. Different first brightness mapping curves are configured based on different scenes.

[0065] Exemplarily, as Figure 2 shown, the method for generating different first brightness mapping curves according to the scene in step S200 includes the following steps:

[0066] Step S211: Set at least three control points, where the at least three control points include a high gray scale threshold point, a low gray scale threshold point, and at least one turning point.

[0067] Under the corresponding scene, different gray scale change trends can be quantified according to customer requirements, industry technologies, etc. for different enhancement targets.

[0068] Exemplarily, a high gray scale threshold point thre_h is configured based on the requirement of high gray scale non-saturation. A low gray scale threshold point thre_l is configured based on the requirement of retaining low gray scale details. At least one turning point is configured for other gray scales based on the enhancement target, and the curve slope of the turning point changes significantly to control the transition effect of highlights or dark parts in the image. Exemplarily, the embodiments of the present application configure two turning points (val_1, val_2), where the turning point val_1 is suitable for a high dynamic scene, and the turning point val_2 is suitable for a low dynamic scene. The abscissa of the low gray scale threshold point thre_l is the first threshold, and the ordinate is the first brightness. And / or the abscissa of the high gray scale threshold point thre_h is the second threshold, and the ordinate is the second brightness. The first brightness is lower than the second brightness, and the first threshold is lower than the second threshold.

[0069] Further, generating the first luminance mapping curve should also avoid banding (banding refers to inaccurate color representation, usually appearing in gradient colors, manifested as obvious color bands or stripes).

[0070] Step S212: Fit a Bézier curve based on at least three control points.

[0071] Quantify the Bézier curve according to the above control points and requirements. Exemplarily, the Bézier curve obtained by quantifying three adjacent control points satisfies the following formula: , , , are three adjacent control points, t is the gray level of the pixel, is the target parameter corresponding to each pixel in the second image obtained by performing global contrast enhancement processing on the first image. Among them, the value range of t is, for example, [0, 1].

[0072] Step S213: Sample the Bézier curve to obtain the first luminance mapping curve.

[0073] Exemplarily, sample the above quantified Bézier curve to obtain the first luminance mapping curve. As Figure 3 shown, the abscissa of the first luminance mapping curve is the pixel value pix1 of the initial image (the first image). Further, the pixel value pix1 of the initial image is, for example, the pixel gray level, and the range of the gray level is obtained by converting t in the Bézier curve. The ordinate of the first luminance mapping curve is the pixel value pix2 of the target image (the second image). Further, the pixel value pix2 of the target image is, for example, the pixel luminance.

[0074] Next, the step of performing global contrast enhancement processing on the first image according to the first luminance mapping curve in step S200 to obtain the second image includes: searching the first luminance mapping curve based on the gray level of each pixel in the first image to generate the adjusted second image. Among them, the adjusted luminance (one of the target parameters) of each pixel in the second image corresponds one-to-one with the gray level of the corresponding pixel in the first image in the first luminance mapping curve.

[0075] Step S300: Divide the first image into several sub-regions, obtain the second luminance mapping curve based on the several sub-regions, and perform local contrast enhancement processing on the first image according to the initial parameters of the pixels in each sub-region, the second luminance mapping curve, and the eigenvalue of the corresponding sub-region to obtain the third image.

[0076] Exemplarily, as Figure 4 shown, step S300 includes the following steps:

[0077] Step S310: Divide the first image into a number of sub-regions.

[0078] Exemplarily, the first image is evenly divided into a number of sub-regions, for example, the sub-regions are 4×4 pixel blocks. Taking a grayscale image of 40×40 pixels as an example, the grayscale image is divided into 100 sub-regions of 10 rows × 10 columns.

[0079] Step S320: Perform histogram statistics on each of the number of sub-regions and interpolate between the sub-regions to obtain a second luminance mapping curve.

[0080] Exemplarily, traverse all the sub-regions obtained by the above division and calculate the eigenvalue of each sub-region (for example, including mean value, variance). Then map the position of each sub-region to two-dimensional coordinates, and use the eigenvalue (for example, mean value) of each sub-region as the interpolation sample of the coordinate point. Then, using the coordinates and eigenvalues of the sub-region as inputs, interpolate to generate a second luminance mapping curve.

[0081] Step S330: Obtain the grayscale spatial distribution feature and grayscale statistical feature of each sub-region.

[0082] In the local contrast enhancement process of the present application, in order to avoid over-processing from introducing grayscale statistical features. In order to achieve texture enhancement, grayscale spatial distribution features are introduced.

[0083] The obtaining of the grayscale spatial distribution feature and grayscale statistical feature of each sub-region includes re-dividing the first image into multiple sub-regions.

[0084] Exemplarily, the sub-region is an image with a size of 4 pixels * 5 pixels, for example. The first image to the sixth image are new images obtained by cropping some pixel points based on the original sub-region. In a possible implementation manner, for example, the pixel points in the last row of each sub-region are cropped to obtain the first image corresponding to each sub-region. The pixel points in the first row of each sub-region are cropped to obtain the second image corresponding to each sub-region. The pixel points in the last column of each sub-region are cropped to obtain the third image corresponding to each sub-region. The pixel points in the first column of each sub-region are cropped to obtain the fourth image corresponding to each sub-region. The pixel points in the last column and the last row of each sub-region are cropped to obtain the fifth image corresponding to each sub-region. The pixel points in the first column and the first row of each sub-region are cropped to obtain the sixth image corresponding to each sub-region. It should be noted that the implementation of the present application is not limited to this.

[0085] Exemplarily, the above grayscale statistical feature is expressed as: , is the initial parameter of each pixel in the sub-region of the first image (for example, the initial luminance), is the average parameter of the pixels in the sub-region (e.g., the average brightness), and n is the number of pixels in the sub-region.

[0086] Exemplarily, the above grayscale spatial distribution feature is expressed as: , where is used to represent the sum of the grayscale differences between the first image and the second image, size_x is used to represent the number of pixel points in a row of the first image and the second image, is used to represent the sum of the grayscale differences between the third image and the fourth image, size_y is used to represent the number of pixel points in a column of the third image and the fourth image, Summ_Diff_xy is used to represent the sum of the grayscale differences between the fifth image and the sixth image, and size_xy is used to represent the sum of the number of pixel points in a row and a column of the fifth image or the sixth image minus the number of overlapping pixel points (in this example, the two overlap one pixel point, so subtract 1). is used to represent the total number of gray levels, can take any positive integer. Exemplarily, for example, it can be 8, 10, 12, etc.

[0087] Step S340: Obtain the target parameter of the corresponding pixel in the third image according to the initial parameter of the pixel in each sub-region, the second brightness mapping curve, the grayscale spatial distribution feature of the corresponding sub-region, and the grayscale statistical feature.

[0088] Exemplarily, the target parameter of the corresponding pixel in the third image satisfies the following formula: ; where is the target parameter of the pixel in the third image, is the first empirical constant, y1 is the initial parameter of the pixel in each sub-region (e.g., the initial brightness), y2 is the adjusted parameter obtained by the corresponding pixel according to the second brightness mapping curve (e.g., the adjusted brightness), , a is the second empirical constant, b is the third empirical constant, is the grayscale spatial distribution feature of the corresponding sub-region, is the grayscale statistical feature of the corresponding sub-region.

[0089] Furthermore, the local contrast adjustment process, for example, may also include restricting the range of the calculated 𝐿𝑜𝑐𝑎l𝑀𝑎𝑝𝑝𝑖𝑛𝑔 within the gray level data range and having a non-decreasing monotonic trend.

[0090] Step S400: Obtain a fourth image with enhanced contrast according to the second image and / or the third image, where at least some of the pixels in the fourth image are obtained by superimposing the corresponding pixels in the second image and the third image.

[0091] In one embodiment, pixels in the fourth image with a gray level lower than the first threshold are replaced with corresponding pixels in the second image, pixels in the fourth image with a gray level higher than the second threshold are replaced with corresponding pixels in the second image, and pixels in the fourth image with a gray level lower than the second threshold and higher than the first threshold are obtained by superimposing corresponding pixels in the second image and the third image. Further, the target parameter of a pixel in the fourth image with a gray level equal to the first threshold is obtained by linearly or non-linearly transitioning based on the target parameters corresponding to adjacent gray levels, and / or the target parameter of a pixel in the fourth image with a gray level equal to the second threshold is obtained by linearly or non-linearly transitioning based on the target parameters corresponding to adjacent gray levels.

[0092] In one embodiment, each pixel in the fourth image is obtained by superimposing corresponding pixels in the second image and the third image.

[0093] Exemplarily, the brightness of at least some pixels in the fourth image obtained by superimposing the second image and the third image satisfies the following formula: ; where is the target parameter of at least some pixels in the fourth image, is the target parameter of the pixels in the third image, is the target parameter of the pixels in the second image, is the first empirical constant, , a is the second empirical constant, b is the third empirical constant, is the gray-scale spatial distribution feature of the corresponding sub-region, is the gray-scale statistical feature of the corresponding sub-region.

[0094] The image processing method provided by this application uses a method of combining local contrast adjustment for at least some pixels with global contrast adjustment. It can not only achieve the purpose of anti-"halo, tomogram" and texture enhancement through local adaptive contrast adjustment, but also coordinate the overall contrast through global contrast adjustment to achieve contrast enhancement for the corresponding scene or various filter effects. Additionally, it can achieve the purpose of anti-flicker in the time domain. This further improves the overall visual effect of the image.

[0095] Figure 5 The flowchart shows another image processing method provided by an embodiment of this application.

[0096] As Figure 5 shown, this embodiment further includes the following steps based on the above image processing method:

[0097] Step S500: Dim the brightness of the fourth image. This step is executed, for example, following step S400. Common brightness dimming processes include, for example, global brightness scaling, gamma correction, local area brightness scaling, brightness scaling of local channels, etc. In this embodiment, global brightness scaling is used to process the fourth image, for example.

[0098] Step S600: Perform high-gray overexposure suppression processing on the first image. This step is executed, for example, following step S100. High-gray overexposure suppression processing can prevent the image from being oversaturated.

[0099] Step S700: Perform color cast protection processing on the fourth image. This step is executed, for example, after step S500 and step S600. Color cast protection avoids or corrects color shifts in the image caused by light, equipment, or processing operations through algorithms to maintain true and natural colors.

[0100] The image processing method provided by this application also performs high-gray overexposure suppression processing, and / or brightness dimming processing, and / or color cast protection processing on the image on the basis of fusing local and global contrast adjustment processing to further improve the visual effect of the image.

[0101] Compared with the first image, the visual effect of the fourth image obtained by using the image processing method of this application is significantly improved. Specifically, the texture at the details is enhanced, and the overall contrast level is adjusted.

[0102] Furthermore, the image processing method of this application can also achieve a stylized filter effect on the basis of realizing the overall contrast adjustment. This is the effect that can be achieved by configuring the first brightness mapping curve.

[0103] The global and local contrast processing provided by this application can be independently switched on and off, and can be combined with the system layer to expand multi-scene visual effect enhancement applications, such as dark room enhancement, sunlight screen effect, etc. It can be combined with the system layer and other IPs to achieve a stylized filter effect.

[0104] Figure 6 A schematic structural diagram of an image processing device according to an embodiment of this application is shown

[0105] As Figure 6 shown, the image processing device 800 includes an acquisition unit 810, a global enhancement unit 820, a local enhancement unit 830, and a processing unit 840. The image processing device 800 can execute the above-disclosed image processing method.

[0106] The acquisition unit 810 acquires the first image to be processed.

[0107] The global enhancement unit 820 generates different first brightness mapping curves according to the scene and performs global contrast enhancement processing on the first image according to the first brightness mapping curve to obtain a second image.

[0108] The local enhancement unit 830 divides the first image into a plurality of sub-regions, obtains a second brightness mapping curve based on the plurality of sub-regions, and performs local contrast enhancement processing on the first image according to the initial parameters of the pixels in each sub-region, the second brightness mapping curve, and the eigenvalue of the corresponding sub-region to obtain a third image.

[0109] The processing unit 840 obtains a fourth image with enhanced contrast according to the second image and / or the third image, wherein at least some of the pixels in the fourth image are obtained by superimposing and processing the corresponding pixels in the second image and the third image.

[0110] This application also provides a chip, for example, including the above-mentioned image processing device. The above chip is, for example, a display driving chip.

[0111] Figure 7 The structural schematic diagram of an electronic device provided according to an embodiment of this application is shown.

[0112] As Figure 7 shown, the electronic device 900 includes one or more of a processing component 910, a memory 920, a power supply component 930, an input / output (I / O) interface 940, and a communication component 950.

[0113] The processing component 910 is used to control the overall operation of the electronic device 900, such as operations associated with display, telephone call, data communication, camera operation, and recording operation. The processing component 910 includes, for example, one or more processors (not shown in the figure) to execute instructions to at least perform the steps of the above image processing method. In addition, the processing component 910 includes, for example, one or more modules to facilitate the interaction between the processing component 910 and other components.

[0114] The memory 920 is configured to store various types of data to support the operation of the electronic device 900, wherein at least the execution instructions required by the processor for performing the above image processing method are stored.

[0115] The power supply component 930 supplies power to various components of the electronic device 900.

[0116] The input / output interface 940 provides an interface between the processing component 910 and the peripheral interface module.

[0117] The communication component 950 is configured to facilitate the communication between the electronic device 900 and other devices in a wired or wireless manner.

[0118] Further, the electronic device 900 may also include, for example, one or more of a sensor component, a multimedia component, and an audio component.

[0119] The multimedia component includes a display panel that provides an output interface between the electronic device 900 and the user.

[0120] The audio component is configured to output and / or input audio signals.

[0121] The sensor component includes one or more sensors for providing a status assessment of various aspects of the electronic device 900.

[0122] The electronic device 900 may be, for example, a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc. The display panel of the electronic device 900 includes at least one of a light-emitting diode display panel, a submillimeter light-emitting diode display panel, a micro light-emitting diode display panel, and a quantum dot light-emitting diode display panel.

[0123] Exemplarily, a non-volatile computer-readable storage medium is also provided, such as a memory 920 including computer program instructions, and the above computer program instructions can be executed by a processor in a processing component 910 in the electronic device 900 to complete the above image processing method.

[0124] It should be noted that the numerical values in this article are only for illustrative purposes. In other embodiments of the present application, other numerical values may also be used to implement the present solution, and specific settings should be made reasonably according to the current situation. The present application does not limit this.

[0125] Finally, it should be noted that: Obviously, the above embodiments are only examples for clearly explaining the present application, and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. And the obvious changes or modifications derived therefrom are still within the protection scope of the present application.

[0126] It should also be understood that the terms and expressions used in this article are only for description, and one or more embodiments of this specification should not be limited to these terms and expressions. Using these terms and expressions does not mean excluding any equivalent features of the illustration and description (or part thereof). It should be recognized that various modifications that may exist should also be included within the scope of the claims. Other modifications, changes, and substitutions may also exist. Correspondingly, the claims should be regarded as covering all these equivalents.

Claims

1. An image processing method, wherein, Comprising: Obtaining a first image to be processed; Generating different first brightness mapping curves according to the scene and performing global contrast enhancement processing on the first image according to the first brightness mapping curve to obtain a second image; Dividing the first image into a plurality of sub-regions, obtaining a second brightness mapping curve based on the plurality of sub-regions, and performing local contrast enhancement processing on the first image according to the initial parameters of the pixels in each sub-region, the second brightness mapping curve, and the eigenvalue of the corresponding sub-region to obtain a third image; Obtaining a fourth image with enhanced contrast according to the second image and / or the third image, wherein at least some pixels in the fourth image are obtained by superimposing the corresponding pixels in the second image and the third image.

2. The image processing method according to claim 1, wherein, At least some pixels in the fourth image being obtained by superimposing the second image and the third image includes: Pixels in the fourth image with a gray level lower than the first threshold value adopt the corresponding pixels in the second image; Pixels in the fourth image with a gray level higher than the second threshold value adopt the corresponding pixels in the second image; Pixels in the fourth image with a gray level lower than the second threshold value and higher than the first threshold value are obtained by superimposing the corresponding pixels in the second image and the third image.

3. The image processing method according to claim 2, wherein, Further comprising: The target parameters of the pixels in the fourth image with a gray level equal to the first threshold value are obtained by linearly or non-linearly transitioning based on the target parameters corresponding to the adjacent gray levels, and / or the target parameters of the pixels in the fourth image with a gray level equal to the second threshold value are obtained by linearly or non-linearly transitioning based on the target parameters corresponding to the adjacent gray levels.

4. The image processing method according to claim 1, wherein, At least some pixels in the fourth image being obtained by superimposing the second image and the third image includes: Each pixel in the fourth image is obtained by superimposing the corresponding pixels in the second image and the third image.

5. The image processing method according to claim 1, wherein, The step of generating different first brightness mapping curves according to the scene includes: Setting at least three control points, the at least three control points including a high gray level threshold point, a low gray level threshold point, and at least one turning point; Fitting a Bézier curve according to the at least three control points; and Sampling the Bézier curve to obtain the first brightness mapping curve.

6. The image processing method according to claim 5, wherein, The abscissa of the low gray level threshold point is the first threshold value, and the ordinate is the first brightness; and / or the abscissa of the high gray level threshold point is the second threshold value, and the ordinate is the second brightness, and the first brightness is lower than the second brightness.

7. The image processing method according to claim 5, wherein, The Bezier curve fitted according to the at least three control points satisfies the following formula: , , , are three adjacent control points, t is the gray level of the pixel, is the target parameter corresponding to each pixel in the second image.

8. The image processing method according to claim 1, wherein, The step of performing local contrast enhancement processing on the first image to obtain a third image further includes: Performing histogram statistics on each of the plurality of sub-regions and interpolating between the sub-regions to obtain the second brightness mapping curve; Obtaining the gray level spatial distribution feature and gray level statistical feature of each sub-region; Obtaining the target parameters of the corresponding pixels in the third image according to the initial parameters of the pixels in each sub-region, the second brightness mapping curve, the gray level spatial distribution feature, and the gray level statistical feature of the corresponding sub-region.

9. The image processing method according to claim 8, wherein, The target parameters of the corresponding pixels in the third image satisfy the following formula: ； wherein, is the target parameter of the pixels in the third image, is the first empirical constant, y1 is the initial parameter of the pixels in each sub-region, and y2 is the adjustment parameter obtained by the corresponding pixels according to the second brightness mapping curve, , a is the second empirical constant, and b is the third empirical constant, is the grayscale spatial distribution feature of the corresponding sub-region, is the grayscale statistical feature of the corresponding sub-region.

10. The image processing method according to claim 1, wherein, The brightness of at least some pixels in the fourth image obtained by superimposing the second image and the third image satisfies the following formula: ; wherein, is the target parameter of at least some pixels in the fourth image, is the target parameter of the pixels in the third image, is the target parameter of the pixels in the second image, is the first empirical constant, , a is the second empirical constant, b is the third empirical constant, is the gray - scale spatial distribution feature of the corresponding sub - region, is the gray - scale statistical feature of the corresponding sub - region.

11. The image processing method according to any one of claims 1 to 10, wherein, Further comprising: Perform a brightness dimming process on the fourth image; and / or perform a high gray overexposure suppression process on the first image; and / or perform a color cast protection process on the fourth image.

12. An image processing apparatus, wherein, It includes: An acquisition unit that acquires a first image to be processed; A global enhancement unit that generates different first brightness mapping curves according to the scene and performs global contrast enhancement processing on the first image according to the first brightness mapping curves to obtain a second image; A local enhancement unit that divides the first image into several sub-regions, obtains a second brightness mapping curve based on the several sub-regions, and performs local contrast enhancement processing on the first image according to the initial parameters of the pixels in each sub-region, the second brightness mapping curve, and the eigenvalue of the corresponding sub-region to obtain a third image; And A processing unit that obtains a fourth image with enhanced contrast according to the second image and / or the third image, wherein at least some pixels in the fourth image are obtained by superimposing and processing the corresponding pixels in the second image and the third image.

13. An electronic device, wherein, It includes: A display panel; A processor; A memory for storing instructions executable by the processor, wherein the processor is configured to execute the image processing method according to any one of claims 1-11.

14. A computer-readable storage medium having a computer program stored thereon, wherein, When the computer program is executed, it implements the image processing method according to any one of claims 1-11.

15. A chip, wherein, It includes the image processing device according to claim 12.

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