Brightness splicing method and system applied to medium and large size screen Demura technology

Abstract

The invention provides a brightness splicing method and system applied to a medium and large-size screen Demura technology, and relates to the technical field of display.The method comprises the steps that firstly, flat-field correction is conducted on a camera through an integrating sphere image, and a relation model of sensor brightness, exposure time and object distance is established; then correcting data acquired by multiple cameras by using the model so as to eliminate the influence of a sensor and an object distance; shooting a to-be-shot product in different regions to obtain sub-pixel coordinates and brightness of each region; calculating a correction coefficient according to the local curvature information, and correcting the regional data; and finally, fusing overlapped parts of adjacent regions to obtain complete full-resolution data. According to the method, the technical blank in the market is filled, the use cost of the industrial camera is reduced, powerful support is provided for high-precision and high-efficiency Demura technology application, and the method is suitable for brightness uniformity correction of medium and large-size and special-shaped screen bodies.

Description

Technical Field

[0001] The present invention relates to the field of display technology, and in particular to a brightness splicing method and system applied to Demura technology for medium and large-sized screens. Background Art

[0002] With the rapid development of display technology, the market has increasingly higher requirements for the visual effects of display screens, especially in the field of medium and large-sized screens, such as smart home, commercial display, and large-sized car screens. More stringent standards are put forward for the fineness and uniformity of display effects. As an effective means of screen brightness and color correction, Demura technology has become one of the key technologies to improve display quality by accurately capturing the luminous information of each area of ​​the screen and achieving uniform adjustment of the screen brightness.

[0003] However, facing the challenges of emerging display products such as medium-to-large-sized, special-shaped, curved and bent screens, traditional Demura technology faces many limitations. First, although the existing high-resolution cameras on the market can meet the acquisition needs of small to medium-sized screens to a certain extent, their field of view and pixel density limitations make it difficult to handle high PPI (Pixels Per Inch) or ultra-aspect ratio screens. Especially for complex products such as automotive large screens and large curvature curved screens, a single camera cannot fully cover them, resulting in missing data and reduced accuracy.

[0004] Secondly, although multi-camera stitching technology can provide a wider field of view, there are still inherent performance differences between cameras of the same brand, including nonlinear brightness response, inconsistency in exposure time, and brightness deviation caused by changes in object distance, etc. These differences will introduce obvious stitching and uneven brightness problems during the image stitching process, seriously affecting the compensation effect of Demura technology, and may even cause new brightness defects on the screen.

[0005] In addition, the complex surface morphology of special-shaped and curved screens places higher demands on the camera's shooting angle and depth of field. Traditional flat shooting methods make it difficult to accurately capture the luminous information of each area of ​​the screen, especially on the curved surface. The brightness changes caused by the depth of field difference further increase the difficulty and error of image stitching.

[0006] In view of the above background, developing a Demura technology suitable for medium and large-sized, special-shaped, curved and bent screens has become a technical challenge that needs to be urgently solved in the display industry. Summary of the invention

[0007] To this end, an embodiment of the present invention provides a brightness splicing method and system applied to Demura technology for medium and large-sized screens, which is used to solve the problem that the existing technology cannot effectively meet the Demura correction needs of medium and large-sized, special-shaped, curved and bent screens, and the data splicing accuracy is low, the stitching seams are obvious, and the brightness is uneven due to differences between cameras, nonlinear brightness response, object distance changes, and depth of field differences on curved surfaces.

[0008] In order to solve the above problem, an embodiment of the present invention provides a brightness splicing method applied to the Demura technology of medium and large-sized screens, the method comprising:

[0009] The camera performs flat field correction by acquiring an integrating sphere image and calculates the evaluation value to confirm the correction effect;

[0010] Fix the brightness of the integrating sphere, set different exposure times, use the calibrated camera to collect the integrating sphere image, and establish the relationship between the brightness of each sensor of the camera and the exposure time;

[0011] Fix the exposure time and integrating sphere brightness, set different object distances, use the calibrated camera to collect integrating sphere images, and establish the relationship between the brightness of each camera sensor and the object distance;

[0012] According to the relationship between exposure time and object distance, a relationship model of the brightness of each sensor of the camera with respect to exposure time and object distance is established;

[0013] The established relationship model is used to correct the data collected by multiple cameras to eliminate the influence of camera sensor and object distance on the data;

[0014] Divide the product to be photographed into multiple areas, use multiple cameras to shoot or use a single camera to move the camera, set the overlap width between the areas, and obtain the sub-pixel coordinates of different areas;

[0015] Extract sub-pixel brightness of different areas according to sub-pixel coordinates;

[0016] For the extracted sub-pixel brightness of different regions, the correction coefficient is calculated according to the local curvature information, and the data of each region is corrected to obtain the correct relative brightness of each region;

[0017] For the surface-corrected data, the overlapping parts of adjacent areas are selected for fusion to obtain complete full-resolution data.

[0018] Preferably, the camera performs flat field correction by collecting an integrating sphere image and calculating an evaluation value to confirm the correction effect, specifically including:

[0019] First, put on the camera lens cap to ensure that no light enters the camera; then use the camera's maximum exposure time and gain setting to shoot to collect dark noise data; then use the camera to capture an integrating sphere image to obtain bright field data; finally, calculate the image evaluation value to confirm the correction effect.

[0020] Preferably, the fixing of the integrating sphere brightness, setting different exposure times, using a rectified camera to collect integrating sphere images, and establishing a relationship between the brightness of each sensor of the camera and the exposure time specifically include:

[0021] The brightness of the integrating sphere is fixed, and a set of integrating sphere images with different exposure times are collected using a calibrated camera;

[0022] The relationship between the brightness of different areas and the exposure time is fitted, the correction coefficient is constructed, and the least squares method is used to solve the correction coefficient, so that the relationship between the brightness of different areas and the exposure time tends to be linear, and the linearity of each sensor of the camera with respect to the exposure time is improved.

[0023] Preferably, the fixed exposure time and the integrating sphere brightness, setting different object distances, using a rectified camera to collect integrating sphere images, and establishing the relationship between the brightness of each sensor of the camera and the object distance specifically include:

[0024] With the brightness and exposure time of the integrating sphere fixed, a set of integrating sphere images at different object distances are collected using a calibrated camera;

[0025] The relationship between the brightness of different areas and the object distance is fitted, a polynomial coefficient matrix is ​​constructed, and the least squares method is used to solve the polynomial coefficient matrix to realize the conversion of any object distance to brightness.

[0026] Preferably, extracting sub-pixel brightness of different areas according to sub-pixel coordinates specifically includes:

[0027] Extract the sub-image data f(x,y) of the light spot from the original camera image according to the sub-pixel coordinates P(x0,y0);

[0028] The point spread function is simulated using a two-dimensional Gaussian function, and its formula is:

[0029]

[0030] Where A represents the peak brightness or amplitude of the light spot; σx and σy represent the standard deviation of the light spot in the x and y directions respectively; exp represents the natural exponential function;

[0031] Taking the logarithm of the above two-dimensional Gaussian function, we get: ln f = a*x 2 +b*y 2 +c*x+

[0032] dy+e;

[0033] in

[0034] In the formula, a, b, c, d, and e all represent coefficients;

[0035] Using the extracted light spot sub-image data f(x, y) and the corresponding coordinates (x, y), the polynomial ln f = a*x is solved by the least squares method. 2 +b*y 2 +c*x+dy+e coefficients a, b, c, d, e;

[0036] According to the coefficients a, b, c, d, and e obtained by solving, the original relationship is brought back to solve the amplitude A of the Gaussian function. The amplitude A is the brightness data of the current sub-pixel coordinate P (x0, y0).

[0037] Preferably, the method of calculating the correction coefficient according to the local curvature information for the extracted sub-pixel brightness of different regions, correcting the data of each region, and obtaining the correct relative brightness of each region specifically includes:

[0038] From the screen design drawing, obtain the local curvature information of each coordinate point P(x,y) on the screen;

[0039] The function C(x,y) is used to represent the local curvature characteristics at the coordinate point P(x,y). Based on the function C(x,y), the correction coefficient function k(x,y) is defined as:

[0040] k(x,y)=1+α*C(x,y)β;

[0041] In the formula, α and β are parameters that need to be fitted through experimental data, which determine the influence of curvature on brightness correction;

[0042] For each pixel position P(x,y) on the screen, use its corresponding correction coefficient k(x,y) to correct the brightness value L(x,y) at that position:

[0043] Lcorrected(x,y)=k(x,y)*L(x,y);

[0044] After the above correction steps, the correct relative brightness Lcorrected(x,y) of each area is obtained.

[0045] Preferably, the data after surface correction is selected to fuse the overlapping parts of adjacent areas to obtain complete full-resolution data, which specifically includes:

[0046] According to the surface-corrected data, the overlapping area of ​​two adjacent areas is determined, assuming that they are area 1 and area 2, and the width of the overlapping area is d;

[0047] Extract the image data of region 1 and region 2 in the overlapping area, and record them as I1(x, y) and I2(x, y) respectively;

[0048] In the overlapped area, a weight coefficient w is defined to control the fusion ratio of I1(x, y) and I2(x, y); assuming that I1 is the left image, d1 represents the distance between the current pixel and the left border of I1; based on d1 and the overlapped area width d, the weight coefficient w is calculated: w = d1 / d;

[0049] Use the weight coefficient w to perform weighted fusion on I1(x,y) and I2(x,y) to obtain the fused image data I(x,y): I(x,y)=w*I1(x,y)+(1-w)*I2(x,y), where I(x,y) represents the pixel value after weighted fusion in the overlapping area;

[0050] The weighted fused overlapping areas are spliced ​​with the non-overlapping parts of the adjacent areas to form complete image data.

[0051] The embodiment of the present invention further provides a brightness splicing system applied to the Demura technology of medium and large-sized screens, and the system is used to implement the brightness splicing method applied to the Demura technology of medium and large-sized screens described above, specifically comprising:

[0052] The camera flat field correction module is used for the camera to perform flat field correction by collecting integrating sphere images and calculating evaluation values ​​to confirm the correction effect;

[0053] The exposure time relationship establishment module is used to fix the brightness of the integrating sphere, set different exposure times, use the calibrated camera to collect the integrating sphere image, and establish the relationship between the brightness of each sensor of the camera and the exposure time;

[0054] The object distance relationship establishment module is used to fix the exposure time and the integrating sphere brightness, set different object distances, use the calibrated camera to collect the integrating sphere image, and establish the relationship between the brightness of each sensor of the camera and the object distance;

[0055] A joint relationship model building module is used to build a relationship model of the brightness of each sensor of the camera with respect to the exposure time and the object distance according to the relationship between the exposure time and the object distance;

[0056] The multi-camera data correction module is used to correct the data collected by multiple cameras using the established relationship model to eliminate the influence of camera sensor and object distance on the data;

[0057] The regional shooting and sub-pixel positioning module is used to divide the product to be photographed into multiple areas, use multiple cameras to shoot or a single camera to move the camera, set the overlap width between the areas, and obtain the sub-pixel coordinates of different areas;

[0058] A sub-pixel brightness extraction module is used to extract sub-pixel brightness of different areas according to sub-pixel coordinates;

[0059] The surface correction module is used to calculate the correction coefficient according to the local curvature information for the extracted sub-pixel brightness of different regions, correct the data of each region, and obtain the correct relative brightness of each region;

[0060] The data fusion module is used to fuse the overlapping parts of adjacent areas of the surface-corrected data to obtain complete full-resolution data.

[0061] An embodiment of the present invention also provides an electronic device, which includes a processor, a memory and a bus system, wherein the processor and the memory are connected through the bus system, the memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the above-mentioned brightness splicing method applied to the Demura technology of medium and large-sized screens.

[0062] An embodiment of the present invention further provides a computer storage medium storing a computer software product, wherein the computer software product includes a number of instructions for enabling a computer device to execute the brightness splicing method for Demura technology applied to medium and large-sized screens as described above.

[0063] It can be seen from the above technical solutions that the present invention has the following beneficial effects:

[0064] (1) The present invention solves the problem that the existing Demura technology does not support medium-sized, large-sized and special-shaped screens, enabling the Demura technology to be applied to medium-sized and large-sized products with high precision, thus filling the technical gap in the market. Through the multi-camera correction scheme and curved surface correction technology, the present invention can handle a wider range of more complex screen types, including medium-sized, large-sized and special-shaped screens.

[0065] (2) The present invention eliminates the mechanical errors of different cameras through a multi-camera correction scheme, thereby improving the accuracy of the collected data. At the same time, the sub-pixel brightness extraction method and the exposure time and object distance correction technology are used to further improve the accuracy and consistency of the data. This enables the present invention to generate more accurate and reliable brightness data, providing a solid foundation for subsequent screen brightness adjustment and optimization.

[0066] (3) The present invention uses a weighted fusion method to gradually merge the overlapping data of adjacent areas, ensuring the integrity and consistency of the final spliced ​​data. This method avoids the generation of splicing errors, makes the brightness distribution of the large screen more uniform, and improves the overall display effect of the screen. At the same time, through precise sub-pixel coordinate conversion and curved surface correction technology, the present invention can also effectively avoid errors in the splicing of camera original images, further improving the splicing quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the implementation cases of the present invention or the technical solutions in the prior art, the following is a brief description of the drawings required for use in the embodiments. By referring to the drawings, the features and advantages of the present invention will be more clearly understood. The drawings are schematic and should not be understood as limiting the present invention in any way. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0068] Figure 1 It is a flow chart of a brightness splicing method applied to Demura technology of medium and large size screens provided in an embodiment;

[0069] Figure 2 : The images before and after flat field correction in the embodiment;

[0070] Figure 3 It is the positioning map collected in the embodiment;

[0071] Figure 4 is a brightness image collected in the embodiment;

[0072] Figure 5 This is a (partial) positioning effect display diagram in the embodiment;

[0073] Figure 6 This is a fusion brightness data diagram in the embodiment;

[0074] Figure 7 It is a block diagram of a brightness splicing system provided in an embodiment and applied to the Demura technology of medium and large-sized screens. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] Embodiment 1

[0077] In order to solve the problem that the existing technology cannot effectively cope with the demura correction requirements of medium and large-sized, special-shaped, curved and bent screens, and the data stitching accuracy is low, the stitching seams are obvious, and the brightness is uneven due to the differences between cameras, nonlinear brightness response, object distance changes, and depth of field differences on curved surfaces. Figure 1 As shown, an embodiment of the present invention proposes a brightness splicing method for Demura technology applied to medium and large-sized screens, the method comprising:

[0078] S1: The camera performs flat field correction by acquiring an integrating sphere image and calculates an evaluation value to confirm the correction effect;

[0079] S2: Fix the brightness of the integrating sphere, set different exposure times, use the calibrated camera to collect the integrating sphere image, and establish the relationship between the brightness of each sensor of the camera and the exposure time;

[0080] S3: Fix the exposure time and the brightness of the integrating sphere, set different object distances, use the calibrated camera to collect the integrating sphere image, and establish the relationship between the brightness of each sensor of the camera and the object distance;

[0081] S4: According to the relationship between exposure time and object distance, a relationship model of the brightness of each sensor of the camera with respect to exposure time and object distance is established;

[0082] S5: Using the established relationship model, the data collected by multiple cameras are corrected to eliminate the influence of camera sensor and object distance on the data;

[0083] S6: Divide the product to be photographed into multiple areas, use multiple cameras to shoot or use a single camera to move the product, set the overlap width between the areas, and obtain the sub-pixel coordinates of different areas;

[0084] S7: extracting sub-pixel brightness of different areas according to sub-pixel coordinates;

[0085] S8: for the extracted sub-pixel brightness of different regions, a correction coefficient is calculated according to the local curvature information, and the data of each region is corrected to obtain the correct relative brightness of each region;

[0086] S9: For the surface-corrected data, select the overlapping parts of adjacent areas for fusion to obtain complete full-resolution data.

[0087] It can be seen from the above technical scheme that the present invention provides a brightness splicing method applied to the Demura technology of medium and large-sized screens, collects integrating sphere images through a multi-camera system for flat field correction, and establishes a relationship model between the brightness of each sensor of the camera and the exposure time and object distance, thereby significantly improving the accuracy and consistency of the brightness data acquisition of medium and large-sized and special-shaped screens. The technology adopts a sub-pixel brightness extraction method, which effectively reduces the influence of external factors on the brightness data and ensures the accuracy of the data. At the same time, for the curved surface part of the vehicle-mounted special-shaped screen, a curvature radius fitting correction scheme is proposed to eliminate the brightness error caused by the curved surface. During the data acquisition process, multiple camera regions collect overlapping transition zones, and gradually merge the overlapping data of adjacent areas through weighted fusion, avoiding the generation of splicing errors. This technical solution not only fills the technical gap in the market, but also reduces the use cost of industrial cameras, provides strong support for the application of high-precision and high-efficiency Demura technology, and is suitable for brightness uniformity correction of medium and large-sized and special-shaped screens.

[0088] Combine the following Figures 2 to 6 , describe the technical solution of the present invention in detail.

[0089] In step S1, the camera performs flat field correction (FFC) by acquiring an integrating sphere image to improve camera uniformity, and calculates an evaluation value to confirm the correction effect, including:

[0090] First, put on the camera's lens cap to make sure no light enters the camera;

[0091] Then take pictures using the camera's maximum exposure time and gain setting to collect dark noise data;

[0092] Then use the camera to capture the integrating sphere image and obtain bright field data (without overexposure, the image mean in the 8-bit image is between 100 and 220);

[0093] The calculation formula for flat field correction is:

[0094]

[0095] In the formula, C represents the corrected image; R represents the image to be corrected; D represents the original image of the dark field camera; m represents the image mean; F represents the original image of the sampled uniform light source;

[0096] Finally, the evaluation value of the image is calculated to confirm the correction effect; by collecting the integrating sphere data evaluationImg again, it is divided into M*N areas, and the mean of each partition is calculated, recorded as lum(m,n), and the evaluation value is calculated If the card control is corrected, proceed to the subsequent steps. If not, repeat the above process.

[0097] In step S2, the brightness of the integrating sphere is fixed, different exposure times are set, and the integrating sphere image is collected using the calibrated camera to establish the relationship between the brightness of each sensor of the camera and the exposure time, which specifically includes:

[0098] With the brightness of the integrating sphere fixed, a set of integrating sphere images with different exposure times are collected using a calibrated camera, where the image exposure time covers a range from low exposure to high exposure;

[0099] Taking efficiency into consideration, the original camera image is downsampled into 4*4 blocks, the relationship between the brightness of different areas and the exposure time is fitted, the correction coefficient is constructed, and the least squares method is used to solve the correction coefficient, so that the relationship between the brightness of different areas and the exposure time tends to be linear, thereby improving the linearity of each camera sensor with respect to the exposure time.

[0100] In step S3, the exposure time and the integrating sphere brightness are fixed, different object distances are set, and the integrating sphere image is collected using the calibrated camera to establish the relationship between the brightness of each sensor of the camera and the object distance, which specifically includes:

[0101] With the brightness and exposure time of the integrating sphere fixed, a set of integrating sphere images at different object distances are collected using a calibrated camera, where the object distance range is 300mm-1200mm (to meet the needs of small to large-sized products);

[0102] Taking efficiency into consideration, the original camera image is downsampled into 4*4 blocks, the relationship between the brightness of different regions and the object distance is fitted, a polynomial coefficient matrix is ​​constructed, and the least squares method is used to solve the polynomial coefficient matrix to realize the conversion of arbitrary object distance to brightness.

[0103] In step S4, according to the relationship between exposure time and object distance, a relationship model of the brightness of each sensor of the camera with respect to exposure time and object distance is established.

[0104] Specifically, according to the data in steps S2 and S3, the relationship between the brightness of each sensor of the camera and the exposure time and object distance is established; Lum(x,y)=F(x,y,t,d), where x is the horizontal coordinate in the camera coordinate system, y is the vertical coordinate in the camera coordinate system, t is the exposure time, and d is the object distance.

[0105] In step S5, the established relationship model is used to correct the data collected by multiple cameras to eliminate the influence of the camera sensor and the object distance on the data.

[0106] In step S6, the product to be photographed is divided into multiple areas, and multiple cameras or a single camera is used for mobile photography. The overlap width is set between the areas, and the sub-pixel coordinates of different areas are obtained, which specifically includes:

[0107] (1) Design an optical solution for multi-camera sampling based on the shape, resolution, PPI and other information of the vehicle's heterogeneous screen to ensure that all areas of the screen are effectively covered and that there is overlapping collection area between adjacent cameras.

[0108] (2) Based on the resolution and shape, etc., a vehicle-mounted multi-camera positioning map is designed, including multiple sub-maps. Each sub-map contains the first feature: an external rectangular frame, the second feature: an array-distributed ellipse, with coordinates recorded as screen_elliptical, and the third feature: an array-distributed RGB single pixel point, with coordinates recorded as Camera_pixel_R, Camera_pixel_G, and Camera_pixel_B.

[0109] (3) Implement sub-pixel positioning algorithm for the positioning map collected by each camera, including:

[0110] The positioning map collected by each camera is subjected to denoising, binarization, morphological and other processing to extract the effective area Mask of the circumscribed rectangle of the first feature.

[0111] In the valid area, the perimeter, area, center of gravity, circumscribed moment and other information of each connected domain in the binary graph are counted. The camera coordinates Camera_elliptical of the second feature (ellipse) and the camera coordinates Camera_pixel_R, Camera_pixel_G, Camera_pixel_B of the third feature (RGB single pixel point) are obtained.

[0112] Use screen_elliptical and Camera_elliptical to create a two-dimensional interpolation mapping function interp2p:

[0113] Camera_elliptical=interp2p(screen_elliptical);

[0114] Bring screen_pixel_G (or other color channels) into the mapping function to get the interpolation result Camera_pixel_G_interp:

[0115] Camera_pixel_G_interp=interp2p(screen_pixel_G);

[0116] Calculate the residual errorG between the interpolation result and the camera coordinates (similarly calculate errorR and errorB):

[0117] errorR=Camera_pixel_R_interp-Camera_pixel_R;

[0118] errorG=Camera_pixel_G_interp-Camera_pixel_G;

[0119] errorB=Camera_pixel_B_interp-Camera_pixel_B;

[0120] Note that the coordinates of all pixels on the screen RGB are screen_all_pixel_R, screen_all_pixel_G, and screen_all_pixel_B. Substitute the mapping function and its error repair to obtain the coordinates of all pixels on the RGB:

[0121] Camera_all_pixel_R_interp=interp2p(screen_all_pixel_R)+errorR;

[0122] Camera_all_pixel_G_interp=interp2p(screen_all_pixel_G)+errorG;

[0123] Camera_all_pixel_B_interp=interp2p(screen_all_pixel_B)+errorB;

[0124] Repeat the above steps to obtain the pixel coordinates in each sub-image as CameraPixel_ij (Camera_all_pixel_R_interp, Camera_all_pixel_G_interp, Camera_all_pixel_B_interp).

[0125] According to the CameraPixel_ij obtained above, merge according to the pixel coordinate index to obtain all pixel coordinate points.

[0126] In step S7, the sub-pixel brightness of different areas is extracted according to the sub-pixel coordinates, which specifically includes:

[0127] Extract the sub-image data f(x,y) of the light spot from the original camera image according to the sub-pixel coordinates P(x0,y0).

[0128] The point spread function is simulated using a two-dimensional Gaussian function, and its formula is:

[0129]

[0130] Where A represents the peak brightness or amplitude of the light spot; σx and σy represent the standard deviation of the light spot in the x and y directions respectively; exp represents the natural exponential function; (x, y) represents the sub-image coordinates; (x0, y0) represents the center coordinates of the light spot;

[0131] Taking the logarithm of the above two-dimensional Gaussian function, we get: ln f = a*x 2 +b*y 2 +c*x+

[0132] dy+e;

[0133] in

[0134] In the formula, a, b, c, d, and e all represent coefficients;

[0135] Using the extracted light spot sub-image data f(x, y) and the corresponding coordinates (x, y), the polynomial ln f = a*x is solved by the least squares method. 2 +b*y 2 +c*x+dy+e coefficients a, b, c, d, e;

[0136] According to the coefficients a, b, c, d, and e obtained by solving, the original relationship is brought back to solve the amplitude A of the Gaussian function. The amplitude A is the brightness data of the current sub-pixel coordinate P (x0, y0).

[0137] In step S8, for the extracted sub-pixel brightness of different regions, the correction coefficient is calculated according to the local curvature information, and the data of each region is corrected to obtain the correct relative brightness of each region, which specifically includes:

[0138] From the screen design drawing, obtain the local curvature information of each coordinate point P(x,y) on the screen;

[0139] The function C(x,y) is used to represent the local curvature characteristics at the coordinate point P(x,y). Based on the function C(x,y), the correction coefficient function k(x,y) is defined as:

[0140] k(x,y)=1+α*C(x,y)β;

[0141] In the formula, α and β are parameters that need to be fitted through experimental data, which determine the influence of curvature on brightness correction;

[0142] For each pixel position P(x,y) on the screen, use its corresponding correction coefficient k(x,y) to correct the brightness value L(x,y) at that position:

[0143] Lcorrected(x,y)=k(x,y)*L(x,y);

[0144] After the above correction steps, the correct relative brightness Lcorrected(x,y) of each area is obtained.

[0145] In step S9, for the surface-corrected data, the overlapping parts of adjacent regions are selected for fusion to obtain complete full-resolution data, which specifically includes:

[0146] According to the surface-corrected data, the overlapping area of ​​two adjacent areas is determined, assuming that they are area 1 and area 2, and the width of the overlapping area is d;

[0147] Extract the image data of region 1 and region 2 in the overlapping area, and record them as I1(x, y) and I2(x, y) respectively;

[0148] In the overlapped area, a weight coefficient w is defined to control the fusion ratio of I1(x, y) and I2(x, y); assuming that I1 is the left image, d1 represents the distance between the current pixel and the left border of I1; based on d1 and the overlapped area width d, the weight coefficient w is calculated: w = d1 / d;

[0149] Use the weight coefficient w to perform weighted fusion on I1(x,y) and I2(x,y) to obtain the fused image data I(x,y): I(x,y)=w*I1(x,y)+(1-w)*I2(x,y), where I(x,y) represents the pixel value after weighted fusion in the overlapping area;

[0150] The weighted fused overlapping areas are spliced ​​with the non-overlapping parts of the adjacent areas to form complete image data.

[0151] Embodiment 2

[0152] like Figure 7 As shown, the present invention provides a brightness splicing system applied to the Demura technology of medium and large-sized screens, and the system is used to implement the brightness splicing method applied to the Demura technology of medium and large-sized screens in the above-mentioned embodiment 1, specifically comprising:

[0153] The camera flat field correction module is used for the camera to perform flat field correction by collecting integrating sphere images and calculating evaluation values ​​to confirm the correction effect;

[0154] The exposure time relationship establishment module is used to fix the brightness of the integrating sphere, set different exposure times, use the calibrated camera to collect the integrating sphere image, and establish the relationship between the brightness of each sensor of the camera and the exposure time;

[0155] The object distance relationship establishment module is used to fix the exposure time and the integrating sphere brightness, set different object distances, use the calibrated camera to collect the integrating sphere image, and establish the relationship between the brightness of each sensor of the camera and the object distance;

[0156] A joint relationship model building module is used to build a relationship model of the brightness of each sensor of the camera with respect to the exposure time and the object distance according to the relationship between the exposure time and the object distance;

[0157] The multi-camera data correction module is used to correct the data collected by multiple cameras using the established relationship model to eliminate the influence of camera sensor and object distance on the data;

[0158] The regional shooting and sub-pixel positioning module is used to divide the product to be photographed into multiple areas, use multiple cameras to shoot or a single camera to move the camera, set the overlap width between the areas, and obtain the sub-pixel coordinates of different areas;

[0159] A sub-pixel brightness extraction module is used to extract sub-pixel brightness of different areas according to sub-pixel coordinates;

[0160] The surface correction module is used to calculate the correction coefficient according to the local curvature information for the extracted sub-pixel brightness of different regions, correct the data of each region, and obtain the correct relative brightness of each region;

[0161] The data fusion module is used to fuse the overlapping parts of adjacent areas of the surface-corrected data to obtain complete full-resolution data.

[0162] The brightness splicing system of the present embodiment applied to the Demura technology for medium and large-sized screens is used to implement the aforementioned brightness splicing method applied to the Demura technology for medium and large-sized screens. Therefore, the specific implementation method of the brightness splicing system applied to the Demura technology for medium and large-sized screens can be seen in the embodiment part of the brightness splicing method applied to the Demura technology for medium and large-sized screens mentioned above. Therefore, its specific implementation method can refer to the description of the corresponding embodiments of each part. In order to avoid redundancy, it will not be repeated here.

[0163] Embodiment 3

[0164] An embodiment of the present invention provides an electronic device, which includes a processor, a memory and a bus system. The processor and the memory are connected through the bus system. The memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the above-mentioned brightness splicing method applied to the Demura technology of medium and large-sized screens.

[0165] Embodiment 4

[0166] An embodiment of the present invention provides a computer storage medium storing a computer software product. The computer software product includes several instructions for enabling a computer device to execute the brightness splicing method for Demura technology applied to medium and large-sized screens.

[0167] 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.

[0168] 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.

[0169] 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 including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0170] 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 them are still within the protection scope of the invention.

Claims

1. A brightness splicing method for Demura technology applied to medium and large size screens, characterized in that: include: The camera performs flat field correction by acquiring an integrating sphere image and calculates the evaluation value to confirm the correction effect; Fix the brightness of the integrating sphere, set different exposure times, use the calibrated camera to collect the integrating sphere image, and establish the relationship between the brightness of each sensor of the camera and the exposure time; Fix the exposure time and integrating sphere brightness, set different object distances, use the calibrated camera to collect integrating sphere images, and establish the relationship between the brightness of each camera sensor and the object distance; According to the relationship between exposure time and object distance, a relationship model of the brightness of each sensor of the camera with respect to exposure time and object distance is established; The established relationship model is used to correct the data collected by multiple cameras to eliminate the influence of camera sensor and object distance on the data; Divide the product to be photographed into multiple areas, use multiple cameras to shoot or use a single camera to move the camera, set the overlap width between the areas, and obtain the sub-pixel coordinates of different areas; Extract sub-pixel brightness of different areas according to sub-pixel coordinates; For the extracted sub-pixel brightness of different regions, the correction coefficient is calculated according to the local curvature information, and the data of each region is corrected to obtain the correct relative brightness of each region; For the surface-corrected data, the overlapping parts of adjacent areas are selected for fusion to obtain complete full-resolution data.

2. The brightness splicing method for Demura technology applied to medium and large-sized screens according to claim 1 is characterized in that: The camera performs flat field correction by collecting an integrating sphere image and calculating an evaluation value to confirm the correction effect, specifically including: First, put on the camera lens cap to ensure that no light enters the camera; then use the camera's maximum exposure time and gain setting to shoot to collect dark noise data; then use the camera to capture an integrating sphere image to obtain bright field data; finally, calculate the image evaluation value to confirm the correction effect.

3. The brightness splicing method for Demura technology applied to medium and large-sized screens according to claim 1 is characterized in that: The fixing of the integrating sphere brightness, setting of different exposure times, using the rectified camera to collect the integrating sphere image, and establishing the relationship between the brightness of each sensor of the camera and the exposure time specifically include: The brightness of the integrating sphere is fixed, and a set of integrating sphere images with different exposure times are collected using a calibrated camera; The relationship between the brightness of different areas and the exposure time is fitted, the correction coefficient is constructed, and the least squares method is used to solve the correction coefficient, so that the relationship between the brightness of different areas and the exposure time tends to be linear, and the linearity of each sensor of the camera with respect to the exposure time is improved.

4. The brightness splicing method for Demura technology applied to medium and large-sized screens according to claim 1, characterized in that: The fixed exposure time and the integrating sphere brightness are set at different object distances, the integrating sphere image is collected by using the calibrated camera, and the relationship between the brightness of each sensor of the camera and the object distance is established, specifically including: With the brightness and exposure time of the integrating sphere fixed, a set of integrating sphere images at different object distances are collected using a calibrated camera; The relationship between the brightness of different areas and the object distance is fitted, a polynomial coefficient matrix is ​​constructed, and the least squares method is used to solve the polynomial coefficient matrix to realize the conversion of any object distance to brightness.

5. The brightness splicing method for Demura technology applied to medium and large size screens according to claim 1 is characterized in that: The step of extracting sub-pixel brightness of different regions according to sub-pixel coordinates specifically includes: Extract the sub-image data f(x,y) of the light spot from the original camera image according to the sub-pixel coordinates P(x0,y0); The point spread function is simulated using a two-dimensional Gaussian function, and its formula is: Where A represents the peak brightness or amplitude of the light spot; σx and σy represent the standard deviation of the light spot in the x and y directions respectively; exp represents the natural exponential function; Taking the logarithm of the above two-dimensional Gaussian function, we get: ln f = a*x 2 +b*y 2 +c*x+dy+e; in In the formula, a, b, c, d, and e all represent coefficients; Using the extracted light spot sub-image data f(x, y) and the corresponding coordinates (x, y), the polynomial ln f = a*x is solved by the least squares method. 2 +b*y 2 +c*x+dy+e coefficients a, b, c, d, e; According to the coefficients a, b, c, d, and e obtained by solving, the original relationship is brought back to solve the amplitude A of the Gaussian function. The amplitude A is the brightness data of the current sub-pixel coordinate P (x0, y0).

6. The brightness splicing method for Demura technology applied to medium and large-sized screens according to claim 1 is characterized in that: The method calculates the correction coefficient according to the local curvature information for the extracted sub-pixel brightness of different regions, corrects the data of each region, and obtains the correct relative brightness of each region, specifically including: From the screen design drawing, obtain the local curvature information of each coordinate point P(x,y) on the screen; The function C(x,y) is used to represent the local curvature characteristics at the coordinate point P(x,y). Based on the function C(x,y), the correction coefficient function k(x,y) is defined as: k(x,y)=1+α*C(x,y)β; In the formula, α and β are parameters that need to be fitted through experimental data, which determine the influence of curvature on brightness correction; For each pixel position P(x,y) on the screen, use its corresponding correction coefficient k(x,y) to correct the brightness value L(x,y) at that position: Lcorrected(x,y)=k(x,y)*L(x,y); After the above correction steps, the correct relative brightness Lcorrected(x,y) of each area is obtained.

7. The brightness splicing method for Demura technology applied to medium and large size screens according to claim 1, characterized in that: The data after surface correction is selected to fuse the overlapping parts of adjacent areas to obtain complete full-resolution data, specifically including: According to the surface-corrected data, the overlapping area of ​​two adjacent areas is determined, assuming that they are area 1 and area 2, and the width of the overlapping area is d; Extract the image data of region 1 and region 2 in the overlapping area, and record them as I1(x, y) and I2(x, y) respectively; In the overlapped area, a weight coefficient w is defined to control the fusion ratio of I1(x, y) and I2(x, y); assuming that I1 is the left image, d1 represents the distance between the current pixel and the left border of I1; based on d1 and the overlapped area width d, the weight coefficient w is calculated: w = d1 / d; Use the weight coefficient w to perform weighted fusion on I1(x,y) and I2(x,y) to obtain the fused image data I(x,y): I(x,y)=w*I1(x,y)+(1-w)*I2(x,y), where I(x,y) represents the pixel value after weighted fusion in the overlapping area; The weighted fused overlapping areas are spliced ​​with the non-overlapping parts of the adjacent areas to form complete image data.

8. A brightness splicing system for medium and large-sized screens using Demura technology, characterized in that: The system is used to implement the brightness splicing method for Demura technology applied to medium and large-sized screens as described in any one of claims 1 to 7, specifically comprising: The camera flat field correction module is used for the camera to perform flat field correction by collecting integrating sphere images and calculating evaluation values ​​to confirm the correction effect; The exposure time relationship establishment module is used to fix the brightness of the integrating sphere, set different exposure times, use the calibrated camera to collect the integrating sphere image, and establish the relationship between the brightness of each sensor of the camera and the exposure time; The object distance relationship establishment module is used to fix the exposure time and the integrating sphere brightness, set different object distances, use the calibrated camera to collect the integrating sphere image, and establish the relationship between the brightness of each sensor of the camera and the object distance; A joint relationship model building module is used to build a relationship model of the brightness of each sensor of the camera with respect to the exposure time and the object distance according to the relationship between the exposure time and the object distance; The multi-camera data correction module is used to correct the data collected by multiple cameras using the established relationship model to eliminate the influence of camera sensor and object distance on the data; The regional shooting and sub-pixel positioning module is used to divide the product to be photographed into multiple areas, use multiple cameras to shoot or a single camera to move the camera, set the overlap width between the areas, and obtain the sub-pixel coordinates of different areas; A sub-pixel brightness extraction module is used to extract sub-pixel brightness of different areas according to sub-pixel coordinates; The surface correction module is used to calculate the correction coefficient according to the local curvature information for the extracted sub-pixel brightness of different regions, correct the data of each region, and obtain the correct relative brightness of each region; The data fusion module is used to fuse the overlapping parts of adjacent areas of the surface-corrected data to obtain complete full-resolution data.

9. An electronic device, characterized in that: The electronic device includes a processor, a memory and a bus system, the processor and the memory are connected through the bus system, the memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the brightness splicing method applied to the Demura technology of medium and large-sized screens as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The computer storage medium stores a computer software product, which includes a number of instructions for enabling a computer device to execute the brightness splicing method applied to the Demura technology for medium and large-sized screens as described in any one of claims 1 to 7.

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