Multi-exposure deviation estimation lookup table construction method, correction method and system

Abstract

The invention relates to a multi-exposure deviation estimation lookup table construction method, correction method and system, and belongs to the technical field of display. Comprising the steps of designing a gray scale gradient graph; performing multi-exposure gradient image acquisition according to the gray scale gradient image to obtain an image data set; screening the image data set to obtain effective gray scale data; normalizing the effective gray-scale data according to respective exposure time, and synthesizing the effective gray-scale data into a complete gray-scale gradient image; performing section projection on the complete gray scale gradual change image to obtain a first Gamma table; normalizing the first Gamma table by using a standard Gamma table to obtain a second Gamma table; calculating the deviation between the standard Gamma table and the second Gamma table; and calculating a brightness correction value according to the respective exposure time and deviation of the effective gray scale data, and obtaining a lookup table. According to the invention, by collecting the accurate relative brightness, the accuracy of the compensation data is improved, so that the compensation effect is optimized.

Description

Technical Field

[0001] The present invention relates to the field of display technology, and in particular to a lookup table construction method, correction method and system for multiple exposure deviation estimation. Background Art

[0002] In the field of demura correction of display panels, shooting low grayscale images has always been a technical challenge, especially in low-brightness areas. Due to the large difference in brightness or chromaticity in low grayscale areas, it is difficult for traditional cameras to capture effective information in both bright and dark areas at a single exposure time. Specifically, there are the following problems: First, the phenomenon of overexposure in bright areas, that is, in higher brightness areas, the camera requires a shorter exposure time. Too long exposure time will cause overexposure in bright areas, making it impossible to accurately obtain brightness information. Second, the problem of underexposure in dark areas, that is, in low brightness areas, the camera requires a longer exposure time. Insufficient exposure time will cause the brightness value of dark area pixels to be close to zero, forming invalid data. Third, selecting an intermediate exposure time will cause overexposure and dark to exist at the same time, causing data distortion. Fourth, the camera has poor response when shooting in the dark state, and the response curve is linear only within a certain range. The response is nonlinear under high exposure, resulting in the inability of the collected data to obtain accurate relative brightness according to the exposure time. In response to the above problems, the existing demura technology requires the use of a single exposure time to shoot the brightness of each pixel to obtain accurate relative brightness. However, for screens with severe low-grayscale uneven brightness, this method cannot collect accurate relative brightness, resulting in deviations in the calculation of demura compensation data and poor compensation effect.

[0003] In addition, in order to improve the dynamic range of the camera, the existing method mainly uses HDR technology. HDR technology fuses multiple exposure images. The key is to fuse the effective values ​​at each exposure time and enhance the overall image to make the light and dark areas of the image more natural. However, the image processed by HDR technology cannot reflect the true relative brightness. When applied to the demura field, the data accuracy is low and the mura compensation effect is poor. Summary of the invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the problem in the prior art that accurate relative brightness cannot be collected, resulting in low accuracy of compensation data and poor compensation effect.

[0005] In a first aspect, in order to solve the above technical problem, the present invention provides a method for constructing a lookup table for estimating multiple exposure deviations, comprising:

[0006] Designing a grayscale gradient map; performing multiple exposure gradient image acquisition according to the grayscale gradient map to obtain an image data set;

[0007] Screening the image data set to obtain valid grayscale data;

[0008] Normalizing the effective grayscale data according to their respective exposure times and synthesizing them into a complete grayscale gradient image;

[0009] Performing cross-sectional projection on the complete grayscale gradient image to obtain a first Gamma table;

[0010] Normalizing the first gamma table by using a standard gamma table to obtain a second gamma table; calculating a deviation between the standard gamma table and the second gamma table;

[0011] According to the exposure time of each of the effective grayscale data and the deviation, a brightness correction value is calculated to obtain a lookup table.

[0012] In one embodiment of the present invention, when calculating the brightness correction value, the calculation formula of the brightness correction value will change according to the type of exposure time, wherein the type of exposure time includes high exposure time, low exposure time, and a range between low exposure and high exposure time.

[0013] In one embodiment of the present invention, during the high exposure time, the calculation formula of the brightness correction value is:

[0014] Lum fit (t) = a0 + a1*t + a2*t 2 +a3*t 3 ;

[0015] Among them, Lum fit (t) is the brightness correction value, a0 is a constant term, a1, a2, a3 are the linear term coefficient, the quadratic term coefficient and the cubic term coefficient respectively, and t is the exposure time.

[0016] In one embodiment of the present invention, during the low exposure time, the calculation formula of the brightness correction value is:

[0017] Lum fit (t) = a0 + a1 * t;

[0018] Among them, Lum fit (t) is the brightness correction value, a0 is the constant term, a1 is the linear term coefficient, and t is the exposure time.

[0019] In one embodiment of the present invention, within the low exposure and high exposure time intervals, the calculation formula for the brightness correction value is:

[0020] Lum fit (t) = a0 + a1*t + a2*t 2 ;

[0021] Among them, Lum fit (t) is the brightness correction value, a0 is the constant term, a1 and a2 are the linear term coefficient and the quadratic term coefficient respectively, and t is the exposure time.

[0022] In one embodiment of the present invention, the method for generating the standard Gamma table is: using an optical instrument to measure the RGBW four-channel brightness value of the center of the screen to obtain the corresponding grayscale of 0 to 255, wherein the screen is a screen that has been repaired for mura.

[0023] In a second aspect, in order to solve the above technical problem, the present invention provides a method for correcting multiple exposure deviation estimation, comprising:

[0024] Perform multiple exposure acquisition on low-brightness grayscale images to obtain grayscale data at different exposure times;

[0025] Using a lookup table constructed by any of the above construction methods, searching for a brightness correction value of each grayscale data, and identifying valid data of the grayscale data;

[0026] The brightness correction value and the effective data are superimposed to obtain a relative brightness image.

[0027] In a third aspect, in order to solve the above technical problem, the present invention provides a correction system for multiple exposure deviation estimation, comprising:

[0028] A data acquisition module is used to perform multiple exposure acquisition on low-brightness grayscale images to obtain grayscale data at different exposure times;

[0029] A search module, used for searching the brightness correction value of each grayscale data according to a lookup table, and identifying valid data of the grayscale data;

[0030] A superposition module, used for superimposing the brightness correction value and the effective data to obtain a relative brightness image;

[0031] The search module includes a lookup table construction module, which is used to design a grayscale gradient map; according to the grayscale gradient map, multiple exposure gradient image acquisition is performed to obtain an image data set; the image data set is screened to obtain valid grayscale data; the valid grayscale data are normalized according to their respective exposure times and synthesized into a complete grayscale gradient image; the complete grayscale gradient image is cross-sectionally projected to obtain a first Gamma table; the first Gamma table is normalized using a standard Gamma table to obtain a second Gamma table; the deviation between the standard Gamma table and the second Gamma table is calculated; the brightness correction value is calculated according to the exposure time of each of the valid grayscale data and the deviation, and the lookup table is obtained.

[0032] In a fourth aspect, in order to solve the above-mentioned technical problem, the present invention provides a display panel, including the above-mentioned correction system for multiple exposure deviation estimation.

[0033] In a fifth aspect, in order to solve the above technical problem, the present invention provides an electronic device, comprising a display panel as mentioned above.

[0034] The above technical solution of the present invention has the following beneficial effects compared with the prior art:

[0035] (1) The method for constructing a lookup table, a correction method and a system for estimating multiple exposure deviations described in the present invention utilize multiple exposures to collect and correct the brightness data in low grayscale images, thereby eliminating the problem of data distortion in bright and dark areas under a single exposure time. The present invention can provide complete and accurate relative brightness data for low grayscale image correction, obtain brightness data with a high dynamic range, improve data accuracy, and thus improve Demura compensation accuracy, solving the abnormal problem when the device collects low grayscale data with severe uneven brightness and darkness.

[0036] (2) The lookup table constructed by the present invention performs real-time brightness correction on low grayscale images, thereby improving data quality. The corrected complete image is used to generate drive compensation parameters, which are used to optimize the brightness and color uniformity of the screen, thereby improving the display effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein

[0038] Figure 1 A flow chart of a method for constructing a lookup table for estimating multiple exposure deviations in a preferred embodiment of the present invention;

[0039] Figure 2 It is a standard Gamma table grayscale diagram in the preferred embodiment of the present invention;

[0040] Figure 3 A grayscale gradient image in a preferred embodiment of the present invention;

[0041] Figure 4 is a gradual image acquired at exposure time T1 in a preferred embodiment of the present invention;

[0042] Figure 5 is a gradual image acquired at exposure time T2 in a preferred embodiment of the present invention;

[0043] Figure 6 is a gradient image acquired at an exposure time TN in a preferred embodiment of the present invention;

[0044] Figure 7 A complete grayscale gradient image in a preferred embodiment of the present invention;

[0045] Figure 8 It is a data curve diagram of the second Gamma table in the preferred embodiment of the present invention;

[0046] Fig. 9 Two Gamma curves drawn for inverse Gamma calculation in a preferred embodiment of the present invention;

[0047] Fig.10 The present invention is a flowchart of a method for correcting multiple exposure deviation estimation in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0049] Embodiment 1

[0050] Reference Figure 1 As shown, an embodiment of the present invention provides a method for constructing a lookup table for estimating multiple exposure deviations, including:

[0051] Design a grayscale gradient map; collect multiple exposure gradient images based on the grayscale gradient map to obtain an image data set;

[0052] Screen the image data set to obtain valid grayscale data;

[0053] The effective grayscale data are normalized according to their respective exposure times and synthesized into a complete grayscale gradient image;

[0054] Perform cross-sectional projection on the complete grayscale gradient image to obtain the first Gamma table;

[0055] Normalizing the first gamma table by using the standard gamma table to obtain a second gamma table; calculating the deviation between the standard gamma table and the second gamma table;

[0056] According to the exposure time and deviation of each effective grayscale data, the brightness correction value is calculated and a lookup table is obtained.

[0057] The embodiment of the present invention provides a method for constructing a lookup table for multiple exposure deviation estimation, which uses multiple exposures to collect and correct the brightness data in low grayscale images, eliminating the problem of data distortion in bright and dark areas under a single exposure time. The method can provide complete and accurate relative brightness data for low grayscale image correction, obtain brightness data with a high dynamic range, improve data accuracy, thereby improving the accuracy of Demura compensation, and solve the abnormal problem when the device collects low grayscale data with severe uneven brightness and darkness.

[0058] Specifically, a response correction coefficient lookup table (LUT) is constructed to achieve data deviation correction and data synthesis under different exposure times. In this embodiment, a camera is used as an example to construct the lookup table. The specific construction steps are as follows:

[0059] Step 1: Camera calibration. Perform a comprehensive calibration of the camera, including flat field correction, dark current correction, and bad pixel correction, to ensure uniformity of the camera chip and image quality.

[0060] Step 2: Generate a standard Gamma table. Select a screen that has been repaired for mura (such as an OLED screen) and use a high-precision optical measurement device (such as CS2000) to measure the RGBW four-channel brightness value at the center of the screen. Based on the measurement results, a standard Gamma table is generated for grayscale values ​​from 0 to 255, expressed as Gamma std The standard gamma table includes Gamma std_R , Gamma std_G , Gamma std_B , Gamma std_W , please refer to Figure 2 shown.

[0061] Step 3: Grayscale gradient pattern design. Design an RGBW (red, green, blue, and white) gradient pattern with grayscale from 0 to 255 to ensure that grayscale data can be fully collected at different exposure times. The design steps include:

[0062] Step 3-1: Determine the total number of gray levels. Usually it is 0 to 255, with a total of 256 gray levels. 0 represents pure black and 255 represents pure white.

[0063] Step 3-2: Select a color mode. The modes include RGB (red, green, and blue) mode and RGBW mode. In this embodiment, the RGBW mode is preferred.

[0064] Step 3-3: Create a grayscale matrix. Create a grayscale matrix for each channel of RGBW. In RGB mode, each channel (red, green, blue) will have 256 grayscale values. In RGBW mode, in addition to the three RGB channels, a white channel is added.

[0065] Step 3-4: Generate a grayscale pattern. Use image processing software (such as Photoshop, GIMP, or a dedicated image generation tool) to create a pattern that contains all grayscales. For RGBW mode, create a 4x256 matrix.

[0066] Step 3-5: Design a gradient effect. Use the gradient tool in the software or write a script to automatically generate a grayscale gradient from 0 to 255. Make sure the gradient is smooth without obvious jumps or discontinuities.

[0067] Step 3-6: After generating the grayscale gradient, check the visual effect of the pattern to ensure that there are no color banding or other visual artifacts. Adjust the smoothness of the gradient as needed. The designed grayscale gradient is similar to Figure 3 shown.

[0068] Step 4: Multi-exposure grayscale image acquisition. Taking the R channel as an example, set multiple exposure times (T1, T2, T3, ..., TN) and shoot the R channel grayscale gradient map N times. Ensure that the data of each grayscale can be accurately acquired at different exposure times to avoid overexposure or underexposure. Generate an image sequence containing random noise (±5), and all image sequences constitute an image data set. The acquired multi-exposure gradient image is similar to Figures 4 to 6 shown.

[0069] Step 5: Image data screening. Screen the validity of the N collected image data. The screening criteria are to select images with brightness values ​​within the range of 20% to 80% of the saturation value, and then obtain valid grayscale data. Among them, the saturation value in image processing and cameras refers to the maximum value that the brightness or signal strength can reach. In digital images, this value is usually related to the bit depth, which determines the number of brightness levels that the image can represent. In an 8-bit image, the saturation value is 2 8 -1 = 255, which means that 256 different brightness levels can be represented from 0 (darkest) to 255 (brightest). In a 10-bit image, the saturation value is 2 10 -1 = 1023, providing a more detailed brightness distinction, with a total of 1024 brightness levels. Similarly, in a 12-bit image, the saturation value is 2 12-1 = 4095, allowing a wider brightness range and finer brightness control. Current industrial cameras can support data acquisition with different bit depths, including 8-bit, 10-bit, and 12-bit. This flexibility allows users to choose the appropriate bit depth according to application requirements to balance image quality and data volume. In this embodiment, an 8-bit (saturation value is 255) image is selected as an example.

[0070] Step 6: Brightness normalization and grayscale image synthesis. Normalize the filtered valid grayscale data and synthesize a complete grayscale gradient image according to their respective exposure times. At the same time, record the exposure time T corresponding to each grayscale. gray The purpose of brightness normalization is to adjust the brightness value of the image (brightness value can also be called display value) to a standard level. This standard level corresponds to the exposure time T gray During the brightness normalization process, each brightness value Lum of the image is divided by the exposure time T of the image. gray In this way, the normalized brightness value L norm It can be expressed as:

[0071]

[0072] Arrange the normalized data in grayscale order to synthesize a complete grayscale gradient image. The complete grayscale gradient image is similar to Figure 7 shown.

[0073] Step 7: Gamma table measurement and normalization. Perform cross-sectional projection on the synthetic image (complete grayscale gradient image) to obtain the actual collected Gamma table, that is, the first Gamma table, denoted as Gamma test_R Since the order of magnitude in the first Gamma table is not the same as that in the standard Gamma table, the measured Gamma table is normalized using the standard Gamma table to generate the second Gamma table, expressed as Gamma normal_R The normalization process compares the data in the measured Gamma table with the data in the standard Gamma table to determine the difference between them. Its purpose is to adjust the measured Gamma value to match the standard Gamma value. In this embodiment, the highest grayscale brightness in the standard Gamma table is selected for normalization, and the data in the generated second Gamma table is as follows: Figure 8 shown.

[0074] Step 8: Calculate the deviation between the standard Gamma table and the second Gamma table. Calculate Gamma std_R Relative Gamma normal_R Deviation Gamma error. The calculated deviation Gamma error As the basis for brightness correction. In this embodiment, an exemplary deviation calculation method is provided, as follows:

[0075] Step 8-1: Perform inverse gamma calculation on the second gamma table and the standard gamma table. Inverse gamma calculation can more accurately evaluate the brightness performance of the image at different gray levels. At the same time, through this calculation process, two gamma curves can be drawn. The specific steps are:

[0076] First, obtain the relationship between brightness value and grayscale in the standard Gamma table, which is:

[0077] Lum=A*grat gamma +B;

[0078] Among them, A is the gain coefficient, B is the residual term, gray is the grayscale, and gamma is the gamma value.

[0079] Secondly, make an approximate inverse Gamma change on the relationship between brightness value and grayscale, and construct the following new relationship:

[0080] Gamma T ≈C*Gray T ;

[0081] Among them, Gamma T is the transformation function to be solved, C is the new coefficient, Gray T is the grayscale value after transformation. For solving the transformation function Gamma T , we can use the least squares method or infinite series estimation method.

[0082] Finally, make the above transformations on the second Gamma table and the standard Gamma table respectively, and draw two Gamma curves, as shown in Fig. 9 This figure shows the difference between the measured value and the standard value at each gray level.

[0083] Step 8-2: After obtaining the two gamma curves, calculate their difference at each grayscale point to determine the error value of each grayscale level. These error values ​​reveal the degree of deviation between the image brightness and the ideal standard at a specific grayscale level. For the difference, the calculation formula is:

[0084] Gamma error =Gamma T -Gamma std .

[0085] Step 8-3: Given the direct relationship between grayscale and exposure time, the brightness deviation Gamma of the image under any exposure time condition is identified error .

[0086] Step 9: Generate correction coefficient lookup table. According to the exposure time T corresponding to different gray levels gray and deviation Gamma error Calculate the brightness correction lookup table. In the correction process of the embodiment of the present invention, the calculation method of the brightness correction value adopts a differentiated strategy for the exposure characteristics of different grayscales. Specifically, the calculation formula of the brightness correction value will change according to the type of exposure time, where the type of exposure time includes high exposure time, low exposure time, and the interval between low exposure and high exposure time. For different types, the specific calculation formula is as follows:

[0087] Case 1: Low grayscale, the exposure time of effective grayscale data is high exposure time. Since the response of the camera shows obvious nonlinear characteristics in this grayscale range, the cubic polynomial fitting method is used to describe the relationship between brightness and exposure time. Specifically, the calculation formula of the brightness correction value is:

[0088] Lum fit (t) = a0 + a1*t + a2*t 2 +a3*t 3 ;

[0089] Among them, Lum fit (t) is the brightness correction value after fitting, a0 is the constant term, a1, a2, a3 are the linear term coefficient, quadratic term coefficient and cubic term coefficient respectively, and t is the exposure time.

[0090] Case 2: Medium to high grayscale, the exposure time of the effective grayscale data is a low exposure time, and the response characteristics of the camera tend to be linear. In order to simplify the calculation and maintain the accuracy of the model, a linear fitting method is used to determine the brightness correction value. Linear fitting assumes that there is a direct linear relationship between the brightness value and the exposure time. Therefore, the calculation formula for the brightness correction value is:

[0091] Lum fit (t) = a0 + a1 * t;

[0092] Case 3: Transition grayscale, the exposure time of the effective grayscale data is within the low exposure and high exposure time intervals. In order to smoothly connect the low exposure and high exposure time intervals, a quadratic polynomial fitting method is used to describe the relationship between the brightness correction value and the exposure time. The quadratic polynomial fitting can provide sufficient flexibility to adapt to the nonlinear changes in the transition area while maintaining the simplicity of the calculation. The calculation formula for the brightness correction value is then:

[0093] Lumfit (t) = a0 + a1*t + a2*t 2 .

[0094] The embodiment of the present invention achieves fine adjustment of image brightness and color through the above steps 1 to 9, from data acquisition to processing, and then to correction and normalization, and significantly improves the image quality. Among them, step 4 ensures the data integrity of bright and dark areas by acquiring the same grayscale image at different exposure times; by adjusting the exposure time, the acquisition range is optimized, and the problem of overexposure or underexposure is effectively avoided, thereby ensuring the comprehensiveness of the image data. Step 5 accurately extracts valid data by setting the brightness range standard. At the same time, invalid data, such as overexposed or dark areas without response, are automatically filtered out, further improving the data quality. Step 6 normalizes the data collected at different exposure times to ensure data consistency. By fusing multiple sets of data, a complete grayscale gradient map is generated, which effectively avoids the brightness fault phenomenon and makes the grayscale transition smoother and more natural. In steps 7 to 8, the measured Gamma table is compared with the standard Gamma table, and normalization is performed, and the brightness deviation is calculated. This process ensures the matching of the corrected image with the standard Gamma curve, and improves the color accuracy and consistency of the image. Step 9 uses different fitting methods for low grayscale, medium and high grayscale, and transition grayscale to correct the nonlinear deviation of the camera response. This differentiated processing method can adjust the brightness more accurately and ensure that the image can maintain high-quality display effects at different grayscales.

[0095] Furthermore, the method provided in the embodiment of the present invention has wide applicability and is applicable to demura correction of various display technologies including OLED, LCD, Micro-LED, etc. Demura correction is a technology used to improve the uniformity of display panels, and the method described in this embodiment is particularly effective when processing low grayscale and low brightness scenes, which can significantly improve the display quality and ensure accurate color and brightness consistency in various display technologies.

[0096] Embodiment 2

[0097] Reference Fig.10 As shown, this embodiment provides a method for correcting multiple exposure deviation estimation, including:

[0098] Perform multiple exposure acquisition on low-brightness grayscale images to obtain grayscale data at different exposure times;

[0099] use Figure 1 The lookup table constructed by the construction method shown is used to find the brightness correction value of each grayscale data and identify the valid data of the grayscale data;

[0100] The brightness correction value and the effective data are superimposed to obtain a relative brightness image.

[0101] The embodiment of the present invention provides a method for correcting multiple exposure deviation estimation, which collects and corrects the brightness data in low grayscale images through multiple exposures. This method effectively solves the problem of data distortion in bright and dark areas that may be caused by a single exposure, and ensures the accuracy and consistency of image brightness. Figure 1 The lookup table constructed by the construction method shown (i.e., the construction method provided in Example 1) performs real-time brightness correction on low grayscale images, significantly improving data quality. The corrected complete image is used to generate drive compensation parameters, which are specifically used to optimize the brightness and chromaticity uniformity of the screen, thereby improving the display effect. The entire process, from data acquisition, processing, brightness correction to generating correction parameters, is fully automated, which not only ensures the consistency of the processing process, but also greatly improves work efficiency. Through this method, the embodiment of the present invention can provide high-quality correction for low grayscale images and meet high-standard display requirements.

[0102] The method for performing multiple exposure acquisition on low brightness grayscale images in this embodiment is similar to step 4 in embodiment 1, and will not be repeated here. In addition, it should be noted that in this method, appropriate multiple exposure technology can be flexibly selected for image acquisition according to different application scenario requirements.

[0103] Embodiment 3

[0104] Based on the same inventive concept, this embodiment provides a correction system for multiple exposure deviation estimation, and its principle of solving the problem is similar to the correction method for multiple exposure deviation estimation provided in Example 2, and the repeated parts will not be repeated.

[0105] This embodiment provides a correction system for multiple exposure deviation estimation, including:

[0106] A data acquisition module is used to perform multiple exposure acquisition on low-brightness grayscale images to obtain grayscale data at different exposure times;

[0107] A search module, used to search for a brightness correction value of each grayscale data according to a lookup table, and identify valid data of the grayscale data;

[0108] A superposition module is used to superimpose the brightness correction value and the effective data to obtain a relative brightness image;

[0109] The search module includes a lookup table construction module, which is used to design a grayscale gradient map; according to the grayscale gradient map, multiple exposure gradient images are collected to obtain an image data set; the image data set is screened to obtain valid grayscale data; the valid grayscale data are normalized according to their respective exposure times and synthesized into a complete grayscale gradient image; the complete grayscale gradient image is cross-sectionally projected to obtain a first Gamma table; the first Gamma table is normalized using a standard Gamma table to obtain a second Gamma table; the deviation between the standard Gamma table and the second Gamma table is calculated; the brightness correction value is calculated according to the exposure time and deviation of the valid grayscale data, and a lookup table is obtained.

[0110] Embodiment 4

[0111] This embodiment provides a display panel, including a multiple exposure deviation estimation correction system provided in the third embodiment.

[0112] Embodiment 5

[0113] This embodiment provides an electronic device, including a display panel provided in the fourth embodiment.

[0114] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0115] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0118] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled 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 list all the implementation methods here. The obvious changes or modifications derived from these are still within the protection scope of the invention.

Claims

1. A method for constructing a lookup table for estimating multiple exposure deviations, characterized in that: include: Design grayscale gradient images; According to the grayscale gradient image, multiple exposure gradient images are collected to obtain an image data set; Screening the image data set to obtain valid grayscale data; Normalizing the effective grayscale data according to their respective exposure times and synthesizing them into a complete grayscale gradient image; Performing cross-sectional projection on the complete grayscale gradient image to obtain a first Gamma table; Normalizing the first gamma table using a standard gamma table to obtain a second gamma table; Calculating the deviation between the standard gamma table and the second gamma table; According to the exposure time of each of the effective grayscale data and the deviation, a brightness correction value is calculated to obtain a lookup table.

2. The method for constructing a lookup table for multiple exposure deviation estimation according to claim 1, characterized in that: When calculating the brightness correction value, the calculation formula of the brightness correction value will change according to the type of the exposure time, wherein the type of exposure time includes high exposure time, low exposure time, and a range between low exposure and high exposure time.

3. The method for constructing a lookup table for multiple exposure deviation estimation according to claim 2, characterized in that: At the high exposure time, the calculation formula of the brightness correction value is: Lum fit (t)=a0+a1*t+a2*t 2 +a3*t 3 ; Among them, Lum fit (t) is the brightness correction value, a0 is a constant term, a1, a2, a3 are the linear term coefficient, the quadratic term coefficient and the cubic term coefficient respectively, and t is the exposure time.

4. The method for constructing a lookup table for multiple exposure deviation estimation according to claim 2, characterized in that: At the low exposure time, the calculation formula of the brightness correction value is: Lum fit (t)=a0+a1*t; Among them, Lum fit (t) is the brightness correction value, a0 is the constant term, a1 is the linear term coefficient, and t is the exposure time.

5. The method for constructing a lookup table for multiple exposure deviation estimation according to claim 2, characterized in that: In the low exposure and high exposure time intervals, the calculation formula for the brightness correction value is: Lum fit (t)=a0+a1*t+a2*t 2 ; Among them, Lum fit (t) is the brightness correction value, a0 is the constant term, a1 and a2 are the linear term coefficient and the quadratic term coefficient respectively, and t is the exposure time.

6. The method for constructing a lookup table for multiple exposure deviation estimation according to claim 1, characterized in that: The method for generating the standard Gamma table is: using an optical instrument to measure the RGBW four-channel brightness value at the center of the screen to obtain grayscales corresponding to 0 to 255, wherein the screen is a screen that has been repaired for mura.

7. A method for correcting multiple exposure deviation estimation, characterized in that: include: Perform multiple exposure acquisition on low-brightness grayscale images to obtain grayscale data at different exposure times; Using a lookup table constructed by any one of the construction methods of claims 1 to 6, searching for a brightness correction value of each grayscale data, and identifying valid data of the grayscale data; The brightness correction value and the effective data are superimposed to obtain a relative brightness image.

8. A correction system for multiple exposure deviation estimation, characterized in that: include: A data acquisition module is used to perform multiple exposure acquisition on low-brightness grayscale images to obtain grayscale data at different exposure times; A search module, used for searching the brightness correction value of each grayscale data according to a lookup table, and identifying valid data of the grayscale data; A superposition module, used for superimposing the brightness correction value and the effective data to obtain a relative brightness image; The search module includes a lookup table construction module, and the lookup table construction module is used to design a grayscale gradient map; According to the grayscale gradient image, multiple exposure gradient images are collected to obtain an image data set; Screening the image data set to obtain valid grayscale data; Normalizing the effective grayscale data according to their respective exposure times and synthesizing them into a complete grayscale gradient image; Performing cross-sectional projection on the complete grayscale gradient image to obtain a first Gamma table; Normalizing the first gamma table using a standard gamma table to obtain a second gamma table; Calculating the deviation between the standard gamma table and the second gamma table; According to the exposure time of each of the effective grayscale data and the deviation, a brightness correction value is calculated to obtain the lookup table.

9. A display panel, characterized in that: A correction system for multiple exposure deviation estimation including the one described in claim 8.

10. An electronic device, characterized in that: A display panel comprising the display panel as claimed in claim 9.

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