Data dynamic splicing method and system for multi-camera curved screen Demura compensation

Abstract

The invention provides a multi-camera curved screen Demura compensation data dynamic splicing method and system, and relates to the technical field of display, and the method comprises the steps: carrying out the flat field correction, dark current correction and dead pixel correction of a plurality of cameras, and obtaining the cameras after uniformity correction; generating a positioning map, collecting the positioning map through a plurality of corrected cameras, and calculating a sub-pixel coordinate mapping relation corresponding to each camera; dynamically estimating the curvature change of the curved screen, constructing a curved surface calibration model and generating a curved surface correction coefficient; performing brightness range correction on the curved surface correction coefficient to obtain a corrected dynamic curved surface correction coefficient; and brightness correction is carried out on gray scale images acquired by the plurality of cameras, and seamless data splicing is realized through brightness calibration among the cameras. The method effectively solves the problem of data acquisition and splicing of the large-size curved screen, is suitable for various display devices, reduces the hardware cost and the process complexity, and has high precision, flexibility and wide application prospects.

Description

Technical Field

[0001] The present invention relates to the field of display technology, and in particular to a data dynamic splicing method and system for demura compensation of a multi-camera curved screen. Background Art

[0002] Driven by the current wave of technological advancement, display technology has made significant progress in many fields, particularly in automotive displays. With the booming automotive industry and rising demand for in-car entertainment experiences, the application scope of automotive displays continues to expand. From traditional instrument clusters to feature-rich central control screens to rear-seat TVs, automotive displays are not only increasing in size but also in form factors, gradually moving towards larger sizes and curved surfaces. While this trend enhances the visual experience, it also presents numerous challenges for mura compensation processes in display technology.

[0003] 1. Camera resolution limitations due to large screen sizes: While market camera resolution has advanced rapidly, it remains insufficient for the ever-increasing size of automotive screens. Even the highest-resolution cameras currently available struggle to capture complete data from large screens. For example, the increasing size of automotive curved OLED screens has surpassed the resolution limits of existing cameras, making it impossible for a single camera to capture complete and accurate screen data. This severely restricts the implementation of mura compensation processes, making it impossible to accurately analyze and correct the brightness and dark characteristics of the screen.

[0004] 2. Optical system problems caused by the characteristics of curved screens: On the one hand, the curvature of curved screens often exceeds the depth of field of the optical system. Because the distance from the camera's photosensitive element to different positions on the curved screen varies, the distance from the camera relative to the center of the optical axis gradually changes, and the brightness of areas outside the depth of field will decrease. This difference in brightness is not caused by the screen's own luminous properties, but by optical system errors. When optical system errors and the screen's inherent brightness and darkness characteristics are mixed, the true brightness and darkness characteristics of the screen cannot be accurately obtained, which makes mura compensation extremely difficult. On the other hand, the increase in product curvature makes the surface shape complex and diverse, and the height difference of the surface further exacerbates the problems of the optical system. The effective area that can be captured by a single camera is extremely limited, and data collection must be carried out with the help of multiple cameras.

[0005] 3. The challenge of stitching data collected by multiple cameras: When exploring multi-camera data acquisition to address the aforementioned issues, new challenges emerged. Different cameras have varying optical system consistency and product characteristics, making seamless stitching of camera-collected data difficult. Differences in camera focus, brightness response, and focus plane position all create issues during the stitching process, affecting the final stitching effect and data accuracy.

[0006] 4. Limitations of existing demura technology: Currently, traditional demura technology mostly relies on a single camera to collect complete screen data to pursue data accuracy. However, as screen size continues to increase and it becomes more difficult to improve camera resolution, this method has gradually become unable to meet demand. For example, Chinese patent CN119364194A discloses a brightness splicing method and system for demura technology applied to medium and large-sized screens. Although it proposes a multi-camera data splicing solution based on correcting optical system errors, it works well for flat screen data splicing. However, when processing curved screen data splicing, the method of calculating the correction coefficient based on the local curvature needs to be preset according to product characteristics, lacks flexibility, and cannot adapt well to complex and diverse curved screen shapes.

[0007] As automotive display technology continues to pursue higher image quality, larger sizes, and curved surfaces, existing technologies have many shortcomings in processing demura compensation data stitching for multi-camera curved screens. An innovative technology is urgently needed to overcome these bottlenecks, achieve more accurate and efficient data stitching and mura compensation, and improve the quality of automotive displays. Summary of the Invention

[0008] To this end, embodiments of the present invention provide a method and system for dynamic data splicing of multi-camera curved screen demura compensation, which are used to address the problems in the prior art of insufficient camera resolution due to the increase in the size of on-board OLED curved screens, difficulty in optical system data acquisition due to the curved surface characteristics, difficulty in seamless splicing of multi-camera data, and poor flexibility in processing curved screen data splicing using existing demura technology.

[0009] In order to solve the above problems, an embodiment of the present invention provides a method for dynamic data splicing of multi-camera curved screen demura compensation, including the following steps:

[0010] Step S1: performing flat field correction, dark current correction, and distortion correction on multiple cameras to obtain uniformity-corrected cameras;

[0011] Step S2: Generate a positioning map and collect it through multiple calibrated cameras, and calculate the sub-pixel coordinate mapping relationship corresponding to each camera;

[0012] Step S3: Based on the sub-pixel coordinate mapping relationship, dynamically estimate the curvature change of the curved screen, build a curved surface calibration model and generate a curved surface correction coefficient;

[0013] Step S4: performing brightness range correction on the curved surface correction coefficient to obtain a corrected dynamic curved surface correction coefficient;

[0014] Step S5: Based on the corrected dynamic surface correction coefficient, brightness correction is performed on the grayscale images captured by multiple cameras, and seamless data splicing is achieved through brightness calibration between cameras.

[0015] Preferably, the dynamically estimating the curvature change of the curved screen, constructing a curved surface calibration model and generating a curved surface correction coefficient specifically includes:

[0016] Select the pixel coordinates of the center area of the sub-pixel coordinate mapping relationship and calculate the horizontal and vertical intervals of the sub-pixels;

[0017] constructing standard equally spaced sub-pixel coordinates based on the intervals, and calculating deviations between the actual sub-pixel coordinates and the standard sub-pixel coordinates;

[0018] The deviation is filtered and then fitted to a surface, which is normalized according to the center of the surface to obtain a surface correction coefficient.

[0019] Preferably, the calculating of the deviation between the actual sub-pixel coordinates and the standard sub-pixel coordinates is specifically: obtaining the position deviation of the actual sub-pixel coordinates relative to the standard sub-pixel coordinates in the horizontal or vertical direction according to the bending direction of the curved surface.

[0020] Preferably, the brightness range correction of the curved surface correction coefficient specifically includes:

[0021] Collect the grayscale image and calculate the horizontal or vertical projection. After filtering, obtain the ratio of the minimum value to the center value of the data as the correction value.

[0022] The surface correction coefficient is superimposed and corrected based on the correction value to obtain a dynamic surface correction coefficient that adapts to the extreme trend of brightness and darkness difference of the actual surface.

[0023] Preferably, the method of achieving seamless data splicing through brightness calibration between cameras specifically includes:

[0024] Select the overlapping area of adjacent cameras along the camera stitching direction and calculate the brightness ratio of the overlapping area;

[0025] The brightness data of adjacent cameras are normalized and corrected based on the brightness ratio, and are stitched together one by one until the fusion of all camera data is completed.

[0026] Preferably, the positioning map is generated and collected through multiple calibrated cameras, and the sub-pixel coordinate mapping relationship corresponding to each camera is calculated. Specifically, based on the sub-pixel coordinate mapping technology of the encoded positioning map, the sub-pixel coordinates Map1, Map2, ..., MapN of each camera partition are obtained, where N represents the number of cameras.

[0027] An embodiment of the present invention further provides a data dynamic splicing system for multi-camera curved screen demura compensation, which is used to implement the above-mentioned data dynamic splicing method for multi-camera curved screen demura compensation, specifically comprising:

[0028] The camera calibration module is used to perform flat field correction, dark current correction, and distortion correction on multiple cameras to obtain uniformity-corrected cameras.

[0029] A positioning map processing module is used to generate a positioning map and collect it through multiple calibrated cameras, and calculate the sub-pixel coordinate mapping relationship corresponding to each camera;

[0030] A surface estimation module, configured to dynamically estimate surface curvature based on the sub-pixel coordinate mapping relationship, construct a surface calibration model, and generate surface correction coefficients;

[0031] A brightness correction module, configured to perform brightness range correction on the curved surface correction coefficient to obtain a dynamic curved surface correction coefficient;

[0032] The data splicing module is used to perform brightness correction on the grayscale image based on the dynamic surface correction coefficient and realize seamless data splicing through brightness calibration between cameras.

[0033] Preferably, the surface estimation module includes:

[0034] An interval calculation unit, configured to select pixel coordinates of a central area of a sub-pixel coordinate mapping relationship and calculate the intervals of the sub-pixels in the horizontal and vertical directions;

[0035] a deviation calculation unit, configured to construct standard equally spaced sub-pixel coordinates based on the intervals, and calculate position deviations between the actual sub-pixel coordinates and the standard sub-pixel coordinates;

[0036] The surface fitting unit is used to filter the deviation and then fit the surface to generate a surface correction coefficient.

[0037] Preferably, the brightness correction module includes:

[0038] A correction value calculation unit is used to collect grayscale images and calculate the projection in the horizontal or vertical direction, and obtain the ratio of the minimum value to the center value of the data as the correction value;

[0039] The coefficient correction unit is used to perform superimposed correction on the surface correction coefficient based on the correction value to obtain the dynamic surface correction coefficient.

[0040] Preferably, the data splicing module includes:

[0041] An overlap region processing unit, configured to select overlap regions of adjacent cameras along a camera stitching direction and calculate a brightness ratio of the overlap regions;

[0042] The successive stitching unit is used to perform normalization correction on the brightness data of adjacent cameras based on the brightness ratio, and stitch together the data of all cameras one by one to complete the fusion.

[0043] It can be seen from the above technical solutions that the present invention has the following beneficial effects:

[0044] (1) Improved stitching accuracy and flexibility: This application solves the rigidity of traditional preset curvature correction coefficients in complex curved screen applications by real-time calibration of the brightness relationship between cameras, dynamic estimation of curved screen curvature changes, and construction of a curved surface calibration model. For example, when faced with automotive OLED curved screens of different sizes and curvatures, it can generate accurate surface correction coefficients based on real-time adjustment based on pixel coordinate offset, making multi-camera data stitching more accurate and adaptable to meet diverse display needs.

[0045] (2) Reduce costs and complexity: No need to rely on ultra-high-resolution cameras or customized optical systems, high-precision data acquisition can be achieved by using multi-camera collaboration and optimization algorithms. On the one hand, it avoids the use of high-cost hardware equipment and reduces hardware procurement costs; on the other hand, it reduces dependence on complex optical systems and reduces process complexity. Taking the detection of in-vehicle display screens as an example, the use of multiple relatively low-cost industrial cameras in conjunction with the algorithm of this application can achieve data acquisition and splicing of large-size curved screens, improving production efficiency while reducing overall costs.

[0046] (3) Enhanced adaptability to application scenarios: This technical solution is applicable to display devices of different sizes and curvatures, such as 40-inch curved screens and 8-inch folding screens in vehicles. Its dynamic model can automatically adjust parameters according to the screen form, breaking through the limitation of a single scenario. Whether it is an in-vehicle display system or a VR / AR display system, it can be applied, greatly expanding the application scope of the technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] 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. Those skilled in the art can derive other drawings based on these drawings without inventive effort. Among them:

[0048] Figure 1 This is a flow chart of a data dynamic splicing method for demura compensation of a multi-camera curved screen provided by the present invention;

[0049] Figure 2 This is the data diagram before and after the flat-field FFC correction of the present invention;

[0050] Figure 3 It is a positioning diagram of the present invention;

[0051] Figure 4 A positioning map collected by the camera of the present invention;

[0052] Figure 5 This is a partial visualization display diagram of pixel maps Map1 and Map2 of the present invention;

[0053] Figure 6 A comparison diagram between the standard sub-pixel coordinate Map_std constructed by the present invention and the actual sub-pixel coordinate Map;

[0054] Figure 7 This is a schematic diagram of position deviation of the present invention;

[0055] Figure 8 Schematic diagram of deviation after filtering of the present invention;

[0056] Figure 9 This is a schematic diagram of projection data of the present invention;

[0057] Figure 10 This is a comparison chart of unprocessed and processed brightness data of the present invention;

[0058] Figure 11 This is a block diagram of a data dynamic splicing system for demura compensation of multi-camera curved screens provided by the present invention. DETAILED DESCRIPTION

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only 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 making creative efforts shall fall within the scope of protection of the present invention.

[0060] Example 1:

[0061] In order to solve the problems in the existing technology of insufficient camera resolution due to the increase in the size of the car-mounted OLED curved screen, the difficulty of optical system data acquisition due to the curved surface characteristics, the difficulty of seamless splicing of multi-camera data, and the poor flexibility of the existing Demura technology in processing curved screen data splicing, such as Figure 1 As shown, the present invention proposes a data dynamic splicing method for multi-camera curved screen demura compensation, the method comprising:

[0062] Step S1: performing flat field correction, dark current correction, and bad pixel correction on multiple cameras to obtain uniformity-corrected cameras;

[0063] Step S2: Generate a positioning map and collect it through multiple calibrated cameras, and calculate the sub-pixel coordinate mapping relationship corresponding to each camera;

[0064] Step S3: Based on the sub-pixel coordinate mapping relationship, dynamically estimate the curvature change of the curved screen, build a curved surface calibration model and generate a curved surface correction coefficient;

[0065] Step S4: performing brightness range correction on the curved surface correction coefficient to obtain a corrected dynamic curved surface correction coefficient;

[0066] Step S5: Based on the corrected dynamic surface correction coefficient, brightness correction is performed on the grayscale images captured by multiple cameras, and seamless data splicing is achieved through brightness calibration between cameras.

[0067] As can be seen from the above technical solution, the present invention proposes a dynamic data splicing method for multi-camera curved screen demura compensation. By performing flat field, dark current, and distortion correction on multiple cameras, the accuracy and stability of camera-collected data are guaranteed. Sub-pixel coordinate mapping technology based on coded positioning maps is used to calculate sub-pixel coordinate mapping relationships, improving positioning accuracy. Dynamic estimation of curved screen curvature changes constructs a calibration model to generate correction coefficients, and brightness range correction is performed, enhancing adaptability to different curved screens and correction flexibility. Finally, the corrected coefficients are used to correct grayscale image brightness and achieve seamless data splicing under inter-camera brightness calibration, eliminating splicing chromatic aberration and brightness discontinuity caused by optical system errors. This method effectively solves the data acquisition and splicing problems of large-scale curved screens, is applicable to a variety of display devices, reduces hardware cost and process complexity, and has high accuracy, flexibility, and broad application prospects.

[0068] In step S1, flat field correction, dark current correction, and bad pixel correction are performed on multiple cameras to obtain cameras after uniformity correction.

[0069] In practical applications, taking the vehicle display inspection scenario as an example, multiple industrial cameras are first selected, such as two 151MVeworks high-resolution industrial cameras (in different scenarios, the appropriate model and number of cameras can be selected based on factors such as screen size and accuracy requirements). When performing flat-field correction on these cameras, let the cameras capture a uniform light source, for example, using a high-precision white exposure calibration plate as a uniform light source. By capturing image data, the flat-field response curve of each camera is calculated, and equalization processing is performed based on this curve, so that the response of each pixel of the camera to the same light tends to be consistent, effectively avoiding the problem of uneven image brightness caused by the camera's own characteristics. The comparison of data before and after correction can intuitively show the correction effect, such as Figure 2 shown.

[0070] Next, perform dark current correction, turn off the light source, and capture a dark current image. Bad pixels are marked in the dark current image. These bad pixels may be caused by manufacturing defects in the camera chip or aging due to long-term use. Use a suitable denoising algorithm to denoise the dark current image, removing noise interference and ensuring the accuracy of subsequent data collection.

[0071] Finally, distortion correction is performed, using a checkerboard calibration method to calculate optical distortion. In a darkroom environment (background illumination <1 lux to minimize the impact of ambient light on test results), the camera captures checkerboard images at different angles and positions using a checkerboard calibration plate. Analysis and calculation of these images derive the camera's intrinsic parameters (such as focal length and principal point position) and extrinsic parameters (such as rotation and translation parameters), thereby correcting the camera's optical distortion and making the captured images more realistic.

[0072] In this embodiment, in step S2, a positioning map is generated and captured by multiple calibrated cameras, and the sub-pixel coordinate mapping relationship corresponding to each camera is calculated.

[0073] Specifically, the present invention is based on the sub-pixel coordinate mapping technology of the coded positioning map (such as the positioning map design proposed by Chinese patent CN119364194A) to produce a positioning map, such as Figure 3 The design of the positioning map needs to take into account the accuracy and convenience of the subsequent calculation of the sub-pixel coordinate mapping relationship. Use multiple calibrated cameras, such as the two Veworks cameras mentioned above, to take pictures of the positioning map, such as Figure 4 shown.

[0074] After shooting is completed, the positioning map data collected by the camera is processed to calculate the sub-pixel coordinate mapping relationship corresponding to each camera. Specifically, the algorithm analyzes the feature information in the positioning map to obtain the sub-pixel coordinates Map1 and Map2 of each camera partition (if there are multiple cameras, Map3, ..., MapN are obtained in sequence). Taking two cameras as an example, the processed pixel maps Map1 and Map2 can be displayed through local visualization, such as Figure 5 As shown, it is convenient to observe and verify the accuracy of the mapping relationship.

[0075] In step S3, based on the sub-pixel coordinate mapping relationship, the curvature change of the curved screen is dynamically estimated, a curved surface calibration model is constructed, and a curved surface correction coefficient is generated.

[0076] Specifically, for each sub-pixel coordinate mapping relationship Map obtained in step S2, select the pixel coordinates of the 5×5 area at the center of the Map. This area of pixels corresponds to the pixel coordinates of the camera optical axis center or the focus plane. Based on the pixel coordinates of this area, calculate the horizontal spacing stepH and the vertical spacing stepV of the sub-pixels.

[0077] According to the calculated intervals stepH and stepV, combined with the Map index, a standard equally spaced sub-pixel coordinate Map_std is constructed, and the actual sub-pixel coordinate Map is compared with the constructed standard sub-pixel coordinate Map_std, such as Figure 6 The deviation D is calculated according to the bending direction of the surface. If the surface is bent in the X direction, the x-coordinate deviation is taken; if it is bent in the Y direction, the y-coordinate deviation is taken, as shown in the figure. Figure 7 shown.

[0078] In order to eliminate the interference factors in the deviation data, the deviation D is filtered. A suitable filtering algorithm, such as Gaussian filtering, can be used to make the deviation data smoother and more accurate. Figure 8 As shown in the figure, the filtered data is subjected to surface fitting, using a least squares method or other fitting algorithm to fit the data into a surface model. Normalization is performed based on the surface center to obtain the surface correction coefficient (coeff), which reflects the curvature of the curved screen and provides a basis for subsequent data correction.

[0079] In step S4, the curved surface correction coefficient is corrected for the brightness range to obtain a corrected dynamic curved surface correction coefficient.

[0080] Specifically, grayscale images are collected. In the vehicle display test, images of different grayscales can be displayed on the display. The horizontal or vertical projection of the collected grayscale images is calculated to obtain projection data, such as Figure 9 As shown in Figure 2. Filtering the projection data removes noise and outliers, making the data more stable and reliable. In the filtered data, the ratio of the minimum value to the center value is obtained and used as the correction value A.

[0081] The correction value A is superimposed on the surface correction coefficient coeff obtained in step S3, and the surface correction coefficient is scaled so that the brightness trend range reaches the extreme trend of the actual surface brightness difference, thereby obtaining the corrected dynamic surface correction coefficient coeff' to ensure the accuracy of subsequent brightness correction.

[0082] In this embodiment, in step S5, brightness correction is performed on the grayscale images captured by multiple cameras based on the corrected dynamic surface correction coefficient, and seamless data splicing is achieved through brightness calibration between cameras.

[0083] Specifically, multiple cameras capture grayscale images from each partition, with the brightness data for each partition recorded as Lum1, Lum2, Lum3, ..., LumN. First, the brightness data for each partition is normalized by its respective exposure time to eliminate brightness differences caused by different camera exposure times.

[0084] Then, the normalized luminance data is corrected using the modified dynamic surface correction coefficient coeff' obtained in step S4 to obtain corrected luminance data Lum1', Lum2', Lum3', . . . , LumN'.

[0085] When performing brightness calibration between cameras, the overlapping areas of adjacent cameras are selected according to the camera stitching direction (horizontal or vertical). The brightness ratio of the overlapping areas is calculated, and the brightness data of adjacent cameras is normalized and corrected based on the brightness ratio. For example, first correct Lum2' according to the brightness ratio of the overlapping areas of Lum1' and Lum2', and splice the corrected Lum2' with Lum1' to obtain Lum_combine; then correct Lum3' according to the brightness ratio of the overlapping areas of Lum_combine and Lum3', and splice it with Lum_combine to obtain a new Lum_combine. According to this rule, stitch one by one along the camera stitching direction until all partitions are stitched together to achieve seamless stitching of multi-camera data, such as Figure 10 As shown, the splicing effect can be displayed intuitively.

[0086] Example 2:

[0087] like Figure 11 As shown, the present invention provides a data dynamic splicing system for multi-camera curved screen demura compensation, which is used to implement the data dynamic splicing method for multi-camera curved screen demura compensation in the first embodiment above, specifically comprising:

[0088] The camera calibration module 100 is used to perform flat field calibration, dark current calibration and distortion calibration on multiple cameras to obtain uniformity-calibrated cameras.

[0089] A positioning map processing module 200 is used to generate a positioning map and collect it through multiple calibrated cameras, and calculate the sub-pixel coordinate mapping relationship corresponding to each camera;

[0090] A surface estimation module 300 is used to dynamically estimate the surface curvature based on the sub-pixel coordinate mapping relationship, construct a surface calibration model and generate surface correction coefficients;

[0091] The brightness correction module 400 is used to correct the brightness range of the curved surface correction coefficient to obtain a dynamic curved surface correction coefficient;

[0092] The data stitching module 500 is used to perform brightness correction on the grayscale image based on the dynamic surface correction coefficient and realize seamless data stitching through brightness calibration between cameras.

[0093] Furthermore, the camera calibration module of the present invention is primarily responsible for performing flat-field, dark current, and distortion correction on multiple cameras. In hardware implementation, this module can be integrated into a dedicated camera control unit, which connects to multiple cameras, sends calibration commands to them, and receives data collected by them.

[0094] In terms of software algorithms, the flat-field correction algorithm calculates the flat-field response curve based on the image data of a uniform light source. The dark current correction algorithm is responsible for collecting dark current images and marking bad pixels and removing noise. The distortion correction algorithm calculates the camera's internal and external parameters and corrects distortion based on the checkerboard calibration method. Through the collaborative work of software and hardware, a camera with uniformity correction is obtained.

[0095] Furthermore, the positioning map processing module of the present invention is used to generate a positioning map and process the positioning map data collected by the camera. When generating the positioning map, the positioning map is generated using graphics generation software based on the sub-pixel coordinate mapping technology of the encoded positioning map, and the positioning map is then displayed on a display device for the camera to capture.

[0096] After the camera collects the positioning map, the calculation unit in this module uses the corresponding algorithm to analyze the positioning map data, calculates the sub-pixel coordinate mapping relationship corresponding to each camera, obtains the sub-pixel coordinates Map1, Map2, ..., MapN of each camera partition, and stores these mapping relationship data in the system's data storage unit for use by subsequent modules.

[0097] Furthermore, the surface estimation module includes an interval calculation unit, a deviation calculation unit, and a surface fitting unit. The interval calculation unit obtains the sub-pixel coordinate mapping relationship Map from the positioning map processing module, selects the pixel coordinates of the central area of the Map, and calculates the sub-pixel intervals stepH and stepV in the horizontal and vertical directions.

[0098] The deviation calculation unit constructs the standard equally spaced sub-pixel coordinate Map_std based on the result of the interval calculation unit, and compares it with the actual sub-pixel coordinate Map, calculates the position deviation D between the actual sub-pixel coordinate and the standard sub-pixel coordinate, and determines whether to calculate the deviation in the horizontal direction or the vertical direction according to the bending direction of the surface.

[0099] The surface fitting unit receives the deviation D output by the deviation calculation unit, filters it, fits the surface using a suitable fitting algorithm, generates a surface correction coefficient coeff, and passes the coefficient to the subsequent brightness correction module.

[0100] Furthermore, the brightness correction module of the present invention includes a correction value calculation unit and a coefficient correction unit. The correction value calculation unit collects grayscale image data, calculates horizontal or vertical projection, filters the projection data, and obtains the ratio of the minimum value to the center value as the correction value A.

[0101] The coefficient correction unit receives the surface correction coefficient coeff generated by the surface estimation module and the correction value A obtained by the correction value calculation unit, superimposes the correction value A on the surface correction coefficient coeff to obtain a dynamic surface correction coefficient coeff' that adapts to the extreme trend of the actual surface brightness difference, and passes the dynamic surface correction coefficient to the data stitching module.

[0102] Furthermore, the data splicing module of the present invention is composed of an overlap region processing unit and a successive splicing unit. The overlap region processing unit selects the overlap region of the data collected by adjacent cameras according to the camera splicing direction and calculates the brightness ratio of the overlap region.

[0103] The successive stitching unit normalizes and corrects the brightness data of adjacent cameras based on the brightness ratios obtained by the overlapping area processing unit. Following the camera stitching order, the brightness data of adjacent cameras is corrected and stitched one by one until all camera data is fused, achieving seamless stitching of multi-camera data and ultimately outputting complete stitched data.

[0104] A data dynamic splicing system for demura compensation of a multi-camera curved screen in this embodiment is used to implement the aforementioned data dynamic splicing method for demura compensation of a multi-camera curved screen. Therefore, the specific implementation methods of the data dynamic splicing system for demura compensation of a multi-camera curved screen can be found in the embodiment part of the data dynamic splicing method for demura compensation of a multi-camera curved screen mentioned above. For example, the camera correction module 100, the positioning map processing module 200, the surface estimation module 300, the brightness correction module 400, and the data splicing module 500 are respectively used to implement steps S1, S2, S3, S4, and S5 in the aforementioned data dynamic splicing method for demura compensation of a multi-camera curved screen. Therefore, its specific implementation methods can refer to the descriptions of the corresponding embodiments of each part. In order to avoid redundancy, they will not be repeated here.

[0105] In order to further illustrate the advantages of the technical solution of the present invention, a description is given below in conjunction with specific experiments.

[0106] (1) Experimental objectives

[0107] In order to verify the effectiveness of the data dynamic splicing method and system for demura compensation of multi-camera curved screens in the present invention, a large-size, high-curvature vehicle OLED screen was used as a test object to evaluate the splicing accuracy and brightness uniformity.

[0108] (2) Test equipment and environment

[0109] A 45-inch ultra-wide curved OLED vehicle display with a curvature radius of R≈1000mm is selected. This display is representative and can better simulate the large-size, high-curvature screen conditions in actual vehicle application scenarios.

[0110] Two 151M Veworks high-resolution industrial cameras, with an aperture of F8, were used for testing in a darkroom environment (background illumination <1 lux) to minimize ambient light interference. A high-precision white exposure calibration plate was also used as an optical calibration tool to ensure accurate camera calibration.

[0111] (3) Implementation steps

[0112] First, perform camera calibration, completing flat field calibration, dark current calibration, and distortion calibration in sequence. The operation steps are consistent with the camera calibration part in the implementation steps of the above method.

[0113] Then, the positioning map is collected and made, and then two Veworks cameras are used to shoot the positioning map respectively. Then, the sub-pixel coordinate mapping relationship is calculated to obtain pixel maps Map1 and Map2.

[0114] Then, the surface brightness is estimated. 5×5 pixels in the center of each map are selected, the horizontal and vertical intervals are calculated, the standard sub-pixel coordinates are constructed, the pixel deviation is calculated, and the surface model is fitted to generate the correction coefficient coeff.

[0115] Finally, brightness correction and stitching are performed, calculating the correction parameter A. The coeff is adjusted to compensate for the difference in brightness and darkness to obtain coeff'. The brightness of each area is corrected, and the brightness stitching between cameras is performed. By comparing the brightness data before and after stitching, it can be intuitively seen that the stitched image has no stitching gaps and the brightness uniformity is significantly improved, effectively verifying the technical effects of the present invention. In practical applications, through quantitative analysis of indicators such as stitching accuracy and brightness uniformity, the advantages of the present invention in stitching demura data for multi-camera curved screens are further demonstrated.

[0116] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can 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 can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0117] 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 produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. 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.

[0118] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 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, thereby providing instructions for implementing the process in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0119] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications derived therefrom remain within the scope of protection of the present invention.

Claims

1. A method for dynamic data splicing of demura compensation for multi-camera curved screens, characterized in that: The following steps are involved: Step S1: performing flat field correction, dark current correction, and distortion correction on multiple cameras to obtain uniformity-corrected cameras; Step S2: Generate a positioning map and collect it through multiple calibrated cameras, and calculate the sub-pixel coordinate mapping relationship corresponding to each camera; Step S3: Based on the sub-pixel coordinate mapping relationship, dynamically estimate the curvature change of the curved screen, build a curved surface calibration model and generate a curved surface correction coefficient; Step S4: performing brightness range correction on the curved surface correction coefficient to obtain a corrected dynamic curved surface correction coefficient; Step S5: Based on the corrected dynamic surface correction coefficient, brightness correction is performed on the grayscale images captured by multiple cameras, and seamless data splicing is achieved through brightness calibration between cameras.

2. The method for dynamic data splicing for demura compensation of multi-camera curved screens according to claim 1, characterized in that: The dynamically estimating the curvature change of the curved screen, constructing a curved surface calibration model and generating a curved surface correction coefficient specifically includes: Select the pixel coordinates of the center area of the sub-pixel coordinate mapping relationship and calculate the horizontal and vertical intervals of the sub-pixels; constructing standard equally spaced sub-pixel coordinates based on the intervals, and calculating deviations between the actual sub-pixel coordinates and the standard sub-pixel coordinates; The deviation is filtered and then fitted to a surface, which is normalized according to the center of the surface to obtain a surface correction coefficient.

3. The method for dynamic data splicing for demura compensation of multi-camera curved screens according to claim 2, characterized in that: The calculation of the deviation between the actual sub-pixel coordinates and the standard sub-pixel coordinates is specifically as follows: according to the bending direction of the curved surface, obtaining the position deviation of the actual sub-pixel coordinates relative to the standard sub-pixel coordinates in the horizontal or vertical direction.

4. The method for dynamic data splicing for demura compensation of multi-camera curved screens according to claim 1, characterized in that: The brightness range correction of the curved surface correction coefficient specifically includes: Collect the grayscale image and calculate the horizontal or vertical projection. After filtering, obtain the ratio of the minimum value to the center value of the data as the correction value. The surface correction coefficient is superimposed and corrected based on the correction value to obtain a dynamic surface correction coefficient that adapts to the extreme trend of brightness and darkness difference of the actual surface.

5. The method for dynamic data splicing for demura compensation of multi-camera curved screens according to claim 1, characterized in that: The method of achieving seamless data splicing through inter-camera brightness calibration specifically includes: Select the overlapping area of adjacent cameras along the camera stitching direction and calculate the brightness ratio of the overlapping area; The brightness data of adjacent cameras are normalized and corrected based on the brightness ratio, and are stitched together one by one until the fusion of all camera data is completed.

6. The method for dynamic data splicing for demura compensation of multi-camera curved screens according to claim 1, characterized in that: The positioning map is generated and collected through multiple calibrated cameras, and the sub-pixel coordinate mapping relationship corresponding to each camera is calculated. Specifically, based on the sub-pixel coordinate mapping technology of the encoded positioning map, the sub-pixel coordinates Map1, Map2, ..., MapN of each camera partition are obtained, where N represents the number of cameras.

7. A data dynamic splicing system for multi-camera curved screen demura compensation, characterized by: The system is used to implement the data dynamic splicing method for demura compensation of a multi-camera curved screen according to any one of claims 1 to 6, specifically comprising: The camera calibration module is used to perform flat field correction, dark current correction, and distortion correction on multiple cameras to obtain uniformity-corrected cameras. A positioning map processing module is used to generate a positioning map and collect it through multiple calibrated cameras, and calculate the sub-pixel coordinate mapping relationship corresponding to each camera; A surface estimation module, configured to dynamically estimate surface curvature based on the sub-pixel coordinate mapping relationship, construct a surface calibration model, and generate surface correction coefficients; A brightness correction module, configured to perform brightness range correction on the curved surface correction coefficient to obtain a dynamic curved surface correction coefficient; The data splicing module is used to perform brightness correction on the grayscale image based on the dynamic surface correction coefficient and realize seamless data splicing through brightness calibration between cameras.

8. The method for dynamic data splicing for demura compensation of multi-camera curved screens according to claim 7, characterized in that: The surface estimation module includes: An interval calculation unit, configured to select pixel coordinates of a central area of a sub-pixel coordinate mapping relationship and calculate the intervals of the sub-pixels in the horizontal and vertical directions; a deviation calculation unit, configured to construct standard equally spaced sub-pixel coordinates based on the intervals, and calculate position deviations between the actual sub-pixel coordinates and the standard sub-pixel coordinates; The surface fitting unit is used to filter the deviation and then fit the surface to generate a surface correction coefficient.

9. The method for dynamic data splicing for demura compensation of a multi-camera curved screen according to claim 7, characterized in that: The brightness correction module includes: A correction value calculation unit is used to collect grayscale images and calculate the projection in the horizontal or vertical direction, and obtain the ratio of the minimum value to the center value of the data as the correction value; The coefficient correction unit is used to perform superimposed correction on the surface correction coefficient based on the correction value to obtain the dynamic surface correction coefficient.

10. The method for dynamic data splicing for demura compensation of multi-camera curved screens according to claim 7, characterized in that: The data splicing module includes: An overlap region processing unit, configured to select overlap regions of adjacent cameras along a camera stitching direction and calculate a brightness ratio of the overlap regions; The successive stitching unit is used to perform normalization correction on the brightness data of adjacent cameras based on the brightness ratio, and stitch together the data of all cameras one by one to complete the fusion.

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