A method, apparatus, and storage medium for correcting viewing angle unevenness in screen image acquisition.

Abstract

This application discloses a method, apparatus, and storage medium for correcting viewing angle unevenness in screen image acquisition, used to improve the accuracy and efficiency of viewing angle unevenness correction for images captured by a screen. When the imaging system is in an offline stage, a backlight is placed in the area where the imaging system is positioned; the backlight is turned on, and an image of the backlight of the imaging system is acquired; the backlight image undergoes flat-field correction processing to generate a backlight correction coefficient image; the imaging system is then switched to an online stage, the screen to be tested is placed on the backlight panel, and images of the screen to be tested under different display grayscale levels are acquired; the image to be corrected is corrected once using the backlight correction coefficient image; a global brightness trend surface image is constructed for the corrected image; viewing angle unevenness correction coefficients are generated based on the image pixel values ​​in the global brightness trend surface image; and viewing angle unevenness correction is performed on the corrected image using the viewing angle unevenness correction coefficients.

Description

Technical Field

[0001] This application relates to the field of display screen inspection, and more particularly to a method, apparatus, and storage medium for correcting uneven viewing angles in screen image acquisition. Background Technology

[0002] In automated AOI (Autonomous Inspection) systems and optical De-Mura (De-Mura) systems for LCD displays, high-precision industrial cameras are used to image the illuminated screen. However, the optical brightness and color of an illuminated LCD display are inherently non-uniform at different viewing angles. The Mura quality inspection standards for displays almost always provide optical parameters at a 0° field of view, introducing significant systematic errors into optical Mura inspection and compensation, resulting in greatly reduced inspection and repair effectiveness. Furthermore, the quantitative analysis of this non-uniformity related to viewing angle is inconsistent across different grayscale levels; generally, lower grayscale levels exhibit more severe viewing angle non-uniformity than higher grayscale levels. This viewing angle non-uniformity also varies across displays with different pixel arrangements or manufacturing processes. Especially as the precision of displays increases, the structure of the displays undergoes significant changes, exacerbating viewing angle non-uniformity. For example, the emergence of flexible screens, foldable screens, and splicing screens has greatly expanded the application areas of displays, but it has also increased the severity of viewing angle non-uniformity to some extent. In addition, displays have undergone improvements between layers, such as the addition of new functional layers (microcircuit layers), the reduction of the thickness of new functional layers, and the improvement of the connection between layers, which further increases the functionality of displays, but may also further increase the severity of viewing angle non-uniformity.

[0003] Currently, most manufacturers of similar optical inspection and compensation systems manually experiment with different correction coefficients under specific shooting conditions, confirming the compensation or inspection effect through visual observation. This method has several drawbacks: firstly, manual experimentation is time-consuming and labor-intensive, and it's difficult to achieve theoretically ideal correction results; secondly, visual observation relies too heavily on human subjective judgment, introducing significant subjective errors into the system. Furthermore, existing calibration methods often depend on specific optical instruments, such as integrating spheres and colorimeters, which not only increases the requirements for the testing environment but also limits the ease of calibration.

[0004] In the existing technology, optical Mura inspection and Mura compensation of display screens sometimes require modeling the luminescence characteristics under different gray levels before detection or compensation. The errors introduced by manual trial and manual confirmation will be further transmitted to the back-end optical modeling process, making the model more susceptible to the influence of viewing angle non-uniformity factors, thus reducing the accuracy of viewing angle non-uniformity correction of images captured by the screen. Summary of the Invention

[0005] This application discloses a method, apparatus, and storage medium for correcting viewing angle unevenness in screen-captured images, which can improve the accuracy and efficiency of viewing angle unevenness correction in screen-captured images.

[0006] In a first aspect, embodiments of this application provide a method for correcting viewing angle unevenness in screen image acquisition, comprising:

[0007] When the imaging system is in the offline stage, a backlight is set up in the placement area of ​​the imaging system; the backlight is turned on, and an image of the backlight of the imaging system is acquired to generate a backlight image; the backlight image is subjected to flat field correction processing to generate a backlight correction coefficient image; the imaging system is adjusted to the online stage, the screen under test is placed on the backlight board, and the images of the screen under test under different display gray levels are acquired; the image to be corrected is corrected once using the backlight correction coefficient image; a global brightness trend surface image of the image to be corrected is constructed for the image to be corrected after the first correction using the surface fitting method; the image pixel values ​​in the global brightness trend surface image are used to generate viewing angle unevenness correction coefficients; the viewing angle unevenness correction coefficients are used to correct the viewing angle unevenness of the image to be corrected after the first correction.

[0008] Optionally, when the imaging system is in the offline stage, after the step of setting a backlight in the placement area of ​​the imaging system, and before the step of turning on the backlight, acquiring an image of the backlight of the imaging system, and generating a backlight image, the viewing angle unevenness correction method further includes: adjusting the light source of the imaging system so that the optical conditions of the imaging system are the same as in the online stage.

[0009] Optionally, the step of performing flat field correction processing on the backlight image to generate a backlight correction coefficient image includes: acquiring a bright field image and a dark field image through an imaging system; performing flat field correction processing on the backlight image based on the bright field image and the dark field image to generate a backlight correction coefficient image.

[0010] Optionally, the step of generating viewing angle unevenness correction coefficients from image pixel values ​​in the global brightness trend surface image includes: performing centering normalization processing and reverse calculation processing on the image pixel values ​​in the global brightness trend surface image to generate viewing angle unevenness correction coefficients.

[0011] Optionally, the screen under test is a foldable screen with a microcircuit region located in the foldable area of ​​the foldable screen. The screen under test is folded to a preset degree during the acquisition of the image to be calibrated. After adjusting the imaging system to the online stage, placing the screen under test on the backlight, and acquiring the image to be calibrated of the screen under test at different display gray levels, and before the step of correcting the image to be calibrated using the backlight correction coefficient image, the viewing angle unevenness correction method further includes: obtaining the maximum and minimum gray levels in the image to be calibrated, obtaining the folding curvature parameter of the screen under test and the reflectivity parameter of the microcircuit region under the folding curvature parameter; generating a contrast adjustment formula based on the maximum gray level, minimum gray level, folding curvature parameter, and reflectivity parameter; and performing visual contrast enhancement processing on the gray level data of the image to be calibrated according to the contrast adjustment formula.

[0012] Optionally, after performing viewpoint unevenness correction on the image to be corrected after one correction using viewpoint unevenness correction coefficients, the viewpoint unevenness correction method further includes: acquiring global brightness trend surface images and viewpoint unevenness correction coefficients for several screens to be tested; performing statistical modeling and parameter fusion based on the global brightness trend surface images and viewpoint unevenness correction coefficients to construct a representative standardized correction coefficient model; and inputting the standardized correction coefficient model into the viewpoint unevenness correction process for auxiliary correction.

[0013] Optionally, after adjusting the imaging system to the online stage, placing the screen under test on the backlight, acquiring images of the screen under test at different display gray levels, and before correcting the image under test using the backlight correction coefficient image, the viewing angle unevenness correction method further includes: performing preprocessing operations on the image under test, including coordinate transformation, geometric correction, Gaussian filtering, and edge correction processing.

[0014] Secondly, embodiments of this application provide a viewing angle unevenness correction device for screen image acquisition, comprising: a setting unit, used to set a backlight in the placement area of ​​the imaging system when the imaging system is in an offline stage; a first generation unit, used to turn on the backlight, acquire an image of the backlight of the imaging system, and generate a backlight image; a second generation unit, used to perform flat field correction processing on the backlight image to generate a backlight correction coefficient image; an acquisition unit, used to adjust the imaging system to an online stage, place the screen to be tested on the backlight board, and acquire images of the screen to be tested under different display gray levels; a correction unit, used to perform a first correction on the image to be corrected using the backlight correction coefficient image; a first construction unit, used to construct a global brightness trend surface image of the image after the first correction using a surface fitting method; a third generation unit, used to generate viewing angle unevenness correction coefficients from the image pixel values ​​in the global brightness trend surface image; and a correction unit, used to perform viewing angle unevenness correction on the image to be corrected after the first correction using the viewing angle unevenness correction coefficients.

[0015] Optionally, after the setting unit and before the first generating unit, the viewing angle unevenness correction device further includes: an adjustment unit for adjusting the light source of the imaging system so that the optical conditions of the imaging system are the same as those in the online stage.

[0016] Optionally, the second generation unit includes: acquiring bright-field images and dark-field images through an imaging system; performing flat-field correction processing on the backlight image based on the bright-field images and dark-field images to generate a backlight correction coefficient image.

[0017] Optionally, the third generation unit includes:

[0018] The image pixel values ​​in the global brightness trend surface image are subjected to centering normalization and inverse calculation to generate the viewing angle unevenness correction coefficient.

[0019] Optionally, the screen under test is a foldable screen with a microcircuit region located in the foldable area of ​​the foldable screen. The screen under test is folded to a preset degree during the acquisition of the image to be corrected. After the acquisition unit and before the correction unit, the viewing angle unevenness correction device further includes: a first acquisition unit, used to acquire the maximum and minimum gray levels in the image to be corrected, acquire the fold curvature parameter of the screen under test and the reflectivity parameter of the microcircuit region under the fold curvature parameter; a fourth generation unit, used to generate a contrast adjustment formula based on the maximum gray level, minimum gray level, fold curvature parameter and reflectivity parameter; and an enhancement unit, used to perform visual contrast enhancement processing on the gray level data of the image to be corrected according to the contrast adjustment formula.

[0020] Optionally, after the correction unit, the viewing angle unevenness correction device further includes: a second acquisition unit, used to acquire global brightness trend surface images and viewing angle unevenness correction coefficients of several screens under test; a second construction unit, used to perform statistical modeling and parameter fusion based on the global brightness trend surface images and viewing angle unevenness correction coefficients to construct a representative standardized correction coefficient model; and an auxiliary correction unit, used to input the standardized correction coefficient model into the viewing angle unevenness correction process for auxiliary correction.

[0021] Optionally, after the acquisition unit and before the correction unit, the viewing angle unevenness correction device further includes a preprocessing unit for performing preprocessing operations on the image to be corrected, including coordinate transformation, geometric correction, Gaussian filtering, and edge correction processing.

[0022] Thirdly, embodiments of this application provide a viewing angle unevenness correction device for screen image acquisition, comprising:

[0023] Processor, memory, input / output units, and bus;

[0024] The processor is connected to memory, input / output units, and a bus;

[0025] The memory stores a program, which the processor calls to execute, such as the first aspect and any optional viewpoint non-uniformity correction method of the first aspect.

[0026] Fourthly, embodiments of this application provide a computer-readable storage medium storing a program that, when executed on a computer, performs the first aspect and any optional viewing angle unevenness correction method of the first aspect.

[0027] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0028] In this application, firstly, when the imaging system is in an offline stage, a backlight is set in the placement area of ​​the imaging system. The backlight is turned on, and an image of the backlight is acquired, generating a backlight image. The backlight image undergoes flat-field correction processing to generate a backlight correction coefficient image. The imaging system is then switched to an online stage, and the screen under test is placed on the backlight panel. Images of the screen under test at different display grayscale levels are acquired. The image under test is corrected using the backlight correction coefficient image. A global brightness trend surface image is constructed for the corrected image using a surface fitting method. Viewing angle unevenness correction coefficients are generated from the image pixel values ​​in the global brightness trend surface image. Viewing angle unevenness correction is then performed on the corrected image using these correction coefficients.

[0029] By setting a backlight adapted to the screen under test and turning it on to emit the corresponding brightness, an image of the backlight is acquired and flat-field correction is performed to generate a backlight correction coefficient image. Next, an image of the screen under test to be corrected is acquired. First, the backlight correction coefficient image is used to eliminate the inhomogeneities of the imaging system itself (lens vignetting, dust spots, uneven light source, sensor response differences). Then, a global brightness trend surface image is constructed using the corrected image. Finally, viewing angle unevenness correction coefficients are generated from the image pixel values ​​in the global brightness trend surface image. Correction is then performed using these viewing angle unevenness correction coefficients, improving the accuracy and efficiency of viewing angle unevenness correction in the captured images of the screen. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a schematic diagram of the first embodiment of the screen image acquisition non-uniformity correction method of this application;

[0032] Figure 2 This is a schematic diagram of the first embodiment of the method for adjusting the light source of the imaging system according to this application;

[0033] Figure 3 A schematic diagram of a first embodiment of the method for generating a backlight correction coefficient image according to this application;

[0034] Figure 4 A schematic diagram of a first embodiment of the method for generating viewing angle non-uniformity correction coefficients for this application;

[0035] Figure 5 This is a schematic diagram of the first embodiment of the visual contrast enhancement processing method of this application;

[0036] Figure 6 This is a schematic diagram of the first embodiment of the auxiliary correction method of this application;

[0037] Figure 7 This is a schematic diagram of a first embodiment of the method for preprocessing an image to be corrected according to this application;

[0038] Figure 8 This is a schematic diagram of the first embodiment of the screen image acquisition viewing angle unevenness correction device of this application;

[0039] Figure 9This is a schematic diagram of a second embodiment of the viewing angle unevenness correction device for screen image acquisition in this application. Detailed Implementation

[0040] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0041] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0042] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0043] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0044] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0045] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0046] In existing technologies, most manufacturers of similar optical inspection and compensation systems manually experiment with different intensities of correction coefficients under specific shooting conditions, confirming the compensation or inspection effect through human observation. This method has several drawbacks: First, manual experimentation is time-consuming and labor-intensive, and it is difficult to obtain theoretically ideal correction results. Second, the method of confirming the effect through human observation is too dependent on human subjective judgment, introducing significant subjective errors into the system. Furthermore, existing correction methods often rely on specific optical instruments, such as integrating spheres and colorimeters, which not only increases the requirements of the testing environment but also limits the convenience of correction. In existing technologies, optical Mura inspection and Mura compensation of displays sometimes require modeling the luminous characteristics at different gray levels before detection or compensation. The errors introduced by manual experimentation and confirmation methods are further propagated to the back-end optical modeling process, making the model more susceptible to the influence of viewing angle non-uniformity factors, thus reducing the accuracy of viewing angle non-uniformity correction in the images captured by the screen.

[0047] Based on this, this application discloses a method, apparatus and storage medium for correcting viewing angle unevenness in screen image acquisition, which can improve the accuracy and efficiency of viewing angle unevenness correction in screen-captured images.

[0048] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0049] The method described in this application can be applied to servers, devices, terminals, or other devices with logical processing capabilities; therefore, this application does not limit its application. For ease of description, the following description uses a terminal as the executing entity.

[0050] Please see Figure 1 This application provides an embodiment of a method for correcting viewing angle unevenness in screen image acquisition, comprising:

[0051] 101. When the imaging system is in the offline stage, a backlight is set in the area where the imaging system is placed.

[0052] While the imaging system is offline, identify the model of the display screen to be captured next and adjust the corresponding backlight to the appropriate position. This ensures that the camera's field of view is entirely illuminated by the background light, preventing other areas from blending into the background. Subsequently, maintain the same optical conditions as during the online phase before capturing backlight images with the camera.

[0053] 102. Turn on the backlight and acquire images of the backlight of the imaging system to generate a backlight image.

[0054] The terminal needs to turn on the backlight with the appropriate brightness according to the type of display screen and the items being detected.

[0055] 103. Perform flat field correction processing on the backlight image to generate a backlight correction coefficient image.

[0056] The terminal performs flat field correction (FFC) on the backlight image to generate a backlight correction coefficient image, which is used to eliminate interference from the imaging system and the backlight environment. It is worth noting that in this process, it is necessary to ensure that the captured backlight completely covers the camera's imaging field of view. In addition, the FFC reference image (backlight correction coefficient image) can be averaged through multiple samplings to improve the stability and representativeness of the reference image.

[0057] 104. Adjust the imaging system to the online stage, place the screen under test on the backlight panel, and collect the images of the screen under test under different display gray levels.

[0058] Next, the terminal places the screen under test at the corresponding position on the backlight and acquires the target image (the image to be calibrated) of the screen under test under different preset display gray levels (such as W225, W192, W128, W60, W25, etc.).

[0059] 105. Correct the image to be corrected by using the backlight correction coefficient image.

[0060] During the online phase, when the camera captures an image of the object to be calibrated containing the object being measured, the image to be calibrated is corrected using the backlight calibration coefficient image. Specifically, this involves multiplying the two images.

[0061] Effect: In areas that were originally dark (low System Response), the Ref(x, y) value corresponding to the backlight correction coefficient image is large (Ref(x, y) refers to the value at the corresponding coordinates), and the brightness is increased after multiplying by the actual image (the image to be corrected). In areas that were originally bright (high System Response), the Ref(x, y) value corresponding to the backlight correction coefficient image is small, and the brightness is suppressed after multiplying by the actual image (the image to be corrected). In the final corrected image, this greatly reduces or eliminates the inhomogeneities of the imaging system itself (lens vignetting, dust spots, uneven light source, sensor response differences). Differences in image brightness mainly (or only) reflect the absorption, scattering, or blocking characteristics of the measured object itself (e.g., changes in object thickness, defects, impurities, etc.).

[0062] 106. Construct a global brightness trend surface image for the image to be corrected after one correction using the surface fitting method.

[0063] In this embodiment, the terminal constructs a global brightness trend surface image of the image to be corrected after one correction using a surface fitting method.

[0064] Specifically, the background grayscale of the image to be corrected can be modeled using a k-th order bivariate polynomial model f. k The (x, y) approximation is high and the reconstruction accuracy is high, so polynomial surface fitting is used to construct the global brightness trend surface of the image after preprocessing.

[0065]

[0066] Where m, n, and k are all integers, and m and n are the orders. To achieve a better balance between computational speed and accuracy, k=3 can be chosen, and the calculation method is as follows. x and y represent the pixel coordinates of the image, ε is the error term, and a 00 a 01 , ..., a 30 These are the coefficients in the polynomial.

[0067] To obtain the global brightness trend surface of the image, least squares regression is used, and the model parameters are solved by minimizing the sum of squared residuals, i.e.:

[0068]

[0069] Where Z = {1, x, y, x} 2 y 2 ,xy,xy 2 x 2 y, x 3 y 3} N×10 N is the total number of pixels in the image, A = [a 00 a 10 a 01 a 20 a 02 a 11 a 12 a 21 a 30 a 03 ] T Let be the coefficient matrix to be determined.

[0070] 107. Generate viewing angle unevenness correction coefficients from the image pixel values ​​in the global brightness trend surface image.

[0071] The terminal generates viewing angle unevenness correction coefficients from the image pixel values ​​in the global brightness trend surface image. That is, by solving the coefficient matrix using the method described above, the global brightness trend surface image, denoted as T(x, y), can be constructed. It is worth noting that the above image brightness trend surface modeling method is only one specific embodiment. Similarly, discrete cosine transform, bicubic B-spline surface fitting, machine learning, and other methods can also be used for modeling.

[0072] 108. Perform viewpoint unevenness correction on the image to be corrected after one correction using the viewpoint unevenness correction coefficient.

[0073] After the viewpoint unevenness correction coefficient is generated, the terminal uses the viewpoint unevenness correction coefficient to perform viewpoint unevenness correction on the image to be corrected after one correction.

[0074] In this embodiment, firstly, when the imaging system is in the offline stage, a backlight is set in the placement area of ​​the imaging system. The backlight is turned on, and an image of the backlight is acquired, generating a backlight image. The backlight image undergoes flat-field correction processing to generate a backlight correction coefficient image. The imaging system is then switched to the online stage, and the screen under test is placed on the backlight panel. Images of the screen under test at different display grayscale levels are acquired. The image under test is corrected using the backlight correction coefficient image. A global brightness trend surface image is constructed for the corrected image using a surface fitting method. Viewing angle unevenness correction coefficients are generated from the image pixel values ​​in the global brightness trend surface image. Viewing angle unevenness correction is then performed on the corrected image using these correction coefficients.

[0075] By setting a backlight adapted to the screen under test, turning on the backlight to emit the corresponding brightness, acquiring a backlight image and performing flat-field correction to generate a backlight correction coefficient image, and then acquiring a correction image of the screen under test, the correction process is as follows: first, the backlight correction coefficient image is used to eliminate the inhomogeneities of the imaging system itself (lens vignetting, dust spots, uneven light source, sensor response differences). Then, a global brightness trend surface image is constructed using the corrected correction image. Finally, viewing angle unevenness correction coefficients are generated from the image pixel values ​​in the global brightness trend surface image. Correction is then performed using these viewing angle unevenness correction coefficients, achieving automated and precise correction of images at different gray levels without manual intervention, thus improving the accuracy and efficiency of viewing angle unevenness correction for images captured of the screen.

[0076] This embodiment provides a set of correction calculation procedures for correcting imaging system errors caused by the non-uniformity of the viewing angle of the LCD screen during the photography process. It can be used to improve the optical measurement accuracy of the LCD screen Mura inspection and compensation system, and further achieve better inspection and compensation effects.

[0077] This embodiment calculates the current grayscale viewing angle unevenness correction coefficient by performing FFC on the backlight and constructing a global brightness trend surface. It realizes automated and accurate correction of images of different grayscale levels without manual intervention, which significantly improves the correction efficiency and adaptability.

[0078] The method presented in this embodiment is convenient to operate, simple and practical, does not rely on a calibration screen with high uniformity, and does not require specific optical instruments.

[0079] Please see Figure 2 This application provides an embodiment of a method for adjusting the light source of an imaging system, comprising:

[0080] 201. Adjust the light source of the imaging system so that the optical conditions of the imaging system are the same as those in the online stage.

[0081] In this embodiment, the terminal adjusts the light source of the imaging system so that the optical conditions of the imaging system are the same as those in the online stage. The purpose is to adjust the imaging lighting environment of the backlight image to be consistent with the image to be corrected. Since different screens under test require different lighting environments, it is necessary to minimize the influence of lighting factors when correcting uneven viewing angles.

[0082] Please see Figure 3 This application provides an embodiment of a method for generating a backlight correction coefficient image, comprising:

[0083] 301. Acquire bright-field and dark-field images using an imaging system.

[0084] 302. Perform flat field correction processing on the backlight image based on the bright field image and dark field image to generate a backlight correction coefficient image.

[0085] In this embodiment, the terminal first acquires bright-field and dark-field images through the imaging system. Then, it performs flat-field correction processing on the backlight image based on the bright-field and dark-field images to generate a backlight correction coefficient image. Specifically, first, an image (dark-field image) is acquired under completely dark conditions (e.g., with the lens covered) to obtain the inherent noise of the pixels (e.g., dark current, thermal noise). This is used to determine the offset value of each pixel, i.e., the output value when there is no light input. Next, a bright-field image is acquired using a uniform light source (e.g., integrating sphere, diffuser) to obtain the pixel response to uniform illumination, which is used to calculate the gain coefficient of each pixel, i.e., the response slope under unit light intensity. Finally, correction is performed using a formula to generate a backlight correction image.

[0086]

[0087] The FFC correction coefficients correspond to the backlight correction coefficients image after flat field correction. The dark field value and bright field value correspond to the dark field image and bright field image, respectively, and the original pixel value corresponds to the backlight image.

[0088] Please see Figure 4 This application provides an embodiment of a method for generating viewpoint non-uniformity correction coefficients, comprising:

[0089] 401. Perform centering normalization and reverse calculation on the image pixel values ​​in the global brightness trend surface image to generate the viewing angle unevenness correction coefficient.

[0090] In this embodiment, the terminal performs centering normalization and reverse calculation on the image pixel values ​​in the global brightness trend surface image to generate the viewing angle unevenness correction coefficient. After the terminal constructs the global brightness trend surface image of the image to be corrected, it performs centering normalization on the image pixel values ​​of the global brightness trend surface image, and then performs reverse calculation to obtain the viewing angle unevenness correction coefficient.

[0091] Specifically, because the FFC processing of the backlight image captured by the camera is completed in the Offline stage, systematic interferences, including uneven imaging system response and non-uniform backlight brightness, are effectively eliminated. Simultaneously, in the Inline stage, a global surface model of the brightness is further performed using an image fitting algorithm, suppressing random interference terms such as dust residue on the screen surface and screen-specific mura. Therefore, the final global brightness trend surface image essentially reflects the brightness variation information caused by the LCD screen structure and its inherent viewing angle characteristics, providing accurate and stable basic data for calculating the viewing angle non-uniformity correction coefficient.

[0092] Using the image center as the viewpoint reference, i.e., using the pixel value at the image center as the unit reference, the global brightness trend surface image is normalized by centering pixel value normalization. The normalized global brightness trend surface image TNorm(x,y) is expressed as follows:

[0093]

[0094] Where T(x, y) is the fitted global brightness trend surface image, (x c y c T(x) represents the center pixel coordinates of the image. c y c T represents the center pixel value of the global brightness trend surface image. Norm (x, y) is the global brightness trend surface image after centering normalization.

[0095] Theoretically, if there were no viewing angle unevenness, the pixel values ​​of the normalized brightness trend image should all be close to 1. However, due to the influence of LCD viewing angle characteristics, the pixel values ​​of the normalized brightness trend image will show areas deviating from 1. Therefore, taking the reciprocal of the pixel values ​​of the normalized brightness trend image will yield the viewing angle unevenness correction coefficient.

[0096]

[0097] Finally, the original image (the image to be corrected) can be corrected based on the calculated uneven view correction coefficient.

[0098] Please see Figure 5 This application provides an embodiment of a method for enhancing visual contrast, comprising:

[0099] 501. Obtain the maximum and minimum gray levels in the image to be corrected, and obtain the folding curvature parameters of the screen to be tested and the reflectivity parameters of the microcircuit region under the folding curvature parameters.

[0100] 502. Generate a contrast adjustment formula based on the maximum gray level, minimum gray level, fold curvature parameter, and reflectivity parameter.

[0101] 503. Perform visual contrast enhancement processing on the grayscale data of the image to be corrected according to the contrast adjustment formula.

[0102] The target type of screen under test is a foldable screen with a microcircuit region. Specifically, the novel display screen in this embodiment integrates a foldable screen structure with a microcircuit layer. The microcircuit region is located in the foldable area of ​​the foldable screen, and the screen under test is folded to a predetermined degree during the acquisition of the reflective fringe pattern. In this embodiment, the novel display screen is a foldable screen with a microcircuit layer within the foldable area. This foldable screen consists of multiple display layers, including a microcircuit layer, a pixel display layer, a protective film layer, etc. The novel display screen has a foldable area, which is typically referred to as the foldable area.

[0103] The novel display incorporates a flexible, foldable structure, allowing pixels to stretch during folding and exhibiting structural recovery capabilities. The microcircuit layer is a layer structure designed to enhance the functionality of each layer of the display. Its core purpose is to increase the functionality of the pixel layer (display layer) and other structural layers. This layer can be configured with microcircuits to precisely control other structural layers, thereby increasing functionality at different levels. In this embodiment, multiple microcircuits (different microcircuits used to implement different layer functions) can be integrated into a single structural layer (microcircuit layer) and added to the novel display's layer structure, essentially adding a new microcircuit layer to the existing layer hierarchy. Because the microcircuits in the microcircuit layer contain a large portion of metal circuitry capable of reflecting light, light reflection occurs in the circuit area. Whether the pixels of the display layer emit light or an external light source illuminates the display, the circuit area reflects light to some extent, resulting in visual unevenness on the display. To reduce this issue, microcircuits in the microcircuit layer are typically placed on the curved edges of the flexible screen (areas with minimal user interaction). However, in foldable screens, which are also flexible screens, the circuitry is usually located in the folded area because it is less likely to affect the user's viewing experience during use. This increases the difficulty of defect detection for these new types of displays. The pixel integration of these new displays is increasingly high, with pixels arranged closely and orderly. In foldable screens, the initial pixel arrangement changes as the curvature of the fold increases, causing the pixels in the folded area to emit light in different directions. However, the circuitry area affects the displayed light source based on its own reflectivity, which is also affected by the curvature. One effect is that curvature increases scattering, weakening the light source in that area (the greater the curvature, the more severe the scattering). The other effect is that curvature enhances the displayed light source. This makes defects in the folded area more susceptible to the influence of both the folded area and the circuitry area.

[0104] Some shallow defects in the folded area are caused by excessive folding, so these defects usually require additional inspection. Often, a pre-set fold curvature is needed to better reveal the features of the folded area. However, shallow features are often difficult to detect with the naked eye, and even with a pre-set curvature, they cannot be accurately captured. Due to the influence of the circuit area and the folded area, the visual contrast enhancement processing of the image to be corrected can be inaccurate. This is because scattering caused by curvature and reflection from the circuit area both affect this region. The shallow features in the folded area of ​​new displays are inherently difficult to detect, thus requiring improvements in visual contrast enhancement processing specifically for the folded area.

[0105] In this embodiment, the terminal needs to obtain the maximum and minimum gray levels in the image to be corrected, the folding curvature parameters of the screen to be tested, and the reflectivity parameters of the microcircuit region under the folding curvature parameters. A new enhancement method is designed using the folding curvature parameters and reflectivity parameters. For novel displays, the following formula can be used:

[0106]

[0107] The image to be corrected after contrast enhancement. Let be the gray value of the image to be corrected at the corresponding coordinate point (x, y). Let be the minimum gray value of the image to be corrected across the entire image. The maximum gray value of the image to be corrected in the entire image. The maximum bendable curvature value of this novel foldable display screen represents the maximum degree of folding of the display screen. This is the current curvature value. This is the maximum reflectivity value of the new foldable display screen, representing the maximum light reflection capability of the new foldable display screen under different degrees of folding and with an external reference light source. Is the current curvature The reflectivity value of the lower circuit region.

[0108] Using the contrast adjustment formula described above, the terminal can perform visual contrast enhancement processing on the grayscale data of the image to be corrected according to the contrast adjustment formula. The image to be corrected incorporates the parameters of curvature and reflection of the circuit area, reducing their influence and making the visual contrast enhancement processing more effective, thereby improving the accuracy of subsequent processing and detection.

[0109] Please see Figure 6 This application provides an embodiment of an auxiliary correction method, comprising:

[0110] 601. Obtain the global brightness trend surface image and viewing angle unevenness correction coefficient of several screens to be tested.

[0111] 602. Based on the global brightness trend surface image and the viewing angle unevenness correction coefficient, statistical modeling and parameter fusion are performed to construct a representative standardized correction coefficient model.

[0112] 603. The standardized correction coefficient model is used as an auxiliary correction in the process of uneven view correction.

[0113] In this embodiment, it is also necessary to optimize and accelerate the viewpoint unevenness correction process and set up a model standardization and reuse mechanism.

[0114] It should be noted that the fitting process of the brightness trend surface and the calculation process of the viewing angle unevenness correction coefficient both support GPU-based accelerated processing or multi-threaded parallel computing, thereby meeting the real-time processing requirements of the online inspection system for high frame rate and low latency. Through parallel design, the processing efficiency of large-size or high-resolution images can be significantly improved, ensuring stable operation of the system in a high-speed production line environment.

[0115] Furthermore, considering that different LCD screens within the same model are highly consistent in terms of structural parameters and material processes, their viewing angle unevenness also exhibits a relatively high degree of consistency. Therefore, this embodiment supports the introduction of a prior learning mechanism, as follows:

[0116] In the initial stages of engineering testing, only a few screens (typically 3-5, with parameters flexibly adjustable) need to be fitted with brightness trend surfaces and have their correction coefficients calculated. Finally, the system uses this data for statistical modeling and parameter fusion to construct a representative, standardized correction coefficient model. In subsequent testing, the system can directly reuse this model or perform rapid interpolation, thus eliminating the need for repetitive fitting processes, further improving overall system efficiency, reducing computational resource consumption, and ensuring the consistency and stability of correction accuracy.

[0117] This embodiment achieves model standardization through an early learning mechanism, which facilitates rapid reuse of the model in subsequent screen calibration, significantly reduces redundant calculations, and improves system processing efficiency.

[0118] Please see Figure 7 This application provides an embodiment of a method for preprocessing an image to be corrected, comprising:

[0119] 701. Perform preprocessing operations on the image to be corrected, including coordinate transformation, geometric correction, Gaussian filtering, and edge correction.

[0120] In this embodiment, the terminal performs preprocessing operations on the image to be corrected, including coordinate transformation, geometric correction, Gaussian filtering, and edge correction.

[0121] Before performing a series of refined operations on the image to be corrected, a comprehensive and meticulous preprocessing is required. The first step is coordinate transformation. During acquisition, transmission, or storage, various factors may cause the image's coordinate system to deviate from the expected standard coordinate system. For example, camera tilt or sensor installation deviations can distort the position and shape of objects in the image, making its coordinate information inaccurate in reflecting the actual scene. Therefore, coordinate transformation is necessary to adjust the image's coordinate system to a unified, standard-compliant system. This process may involve mathematical transformations such as translation, rotation, and scaling. Using relevant transformation matrices and algorithms, the position of each pixel in the new coordinate system is precisely calculated, ensuring the image's spatial positioning accuracy and providing a correct coordinate reference for subsequent geometric correction and other processing steps.

[0122] The next step is geometric correction. During image acquisition, images are often affected by various factors such as lens distortion, terrain undulations, and sensor characteristics, resulting in geometric distortion. This distortion manifests as the shape, size, and position of objects in the image not matching the actual scene; for example, straight lines become curves, and rectangles become distorted. The purpose of geometric correction is to eliminate these geometric distortions, restoring the image to its true geometric shape and spatial relationships. In practice, control points with known and accurate locations in the image need to be selected. These control points can be manually marked or obtained through other auxiliary methods. Then, a geometric transformation model, such as a polynomial transformation model or an affine transformation model, is established based on these control points. This model is then used to reposition and interpolate each pixel in the image, resulting in a geometrically corrected image that better reflects actual geographical or physical spatial relationships.

[0123] Gaussian filtering is another crucial step in preprocessing. During image acquisition and transmission, various types of noise, such as Gaussian noise and salt-and-pepper noise, are inevitably introduced. These noises severely impact image quality and subsequent processing, such as reducing image sharpness and interfering with edge detection. Gaussian filtering, a commonly used linear smoothing method, effectively suppresses Gaussian noise. Its principle involves generating a convolution kernel using a Gaussian function and then convolving this kernel with the image. During convolution, the value of each pixel is replaced by the weighted average of its neighboring pixel values, with the weights determined by the Gaussian function; pixels closer to the center pixel have a higher weight. In this way, Gaussian filtering can smooth the image while preserving as much edge and detail information as possible, reducing noise interference and improving the signal-to-noise ratio.

[0124] Finally, there is edge correction processing. During image acquisition, transmission, and preprocessing, edge information may be damaged or distorted to some extent. Edges are the boundaries between different regions in an image, containing rich information about object shape and structure, which is crucial for subsequent image analysis and recognition. Edge correction processing aims to restore and enhance the edge information of an image, making it clearer and more accurate. This process may involve operations such as optimizing edge detection algorithms, edge joining, and thinning. For example, by adjusting the threshold of the edge detection algorithm, edges in the image can be extracted more accurately; edge joining algorithms can connect broken edges to form complete edge contours; and edge thinning operations can reduce the edge width to one pixel, highlighting the detailed features of the edge. Through these edge correction processing techniques, the quality and accuracy of image edges can be improved, providing more reliable edge information for subsequent tasks such as feature extraction and object recognition.

[0125] In summary, preprocessing operations on the image to be corrected, including coordinate transformation, geometric correction, Gaussian filtering, and edge correction, are key steps to ensure image quality and the effectiveness of subsequent processing. They are also of great significance for subsequent image analysis and target recognition.

[0126] Please see Figure 8 This application provides an embodiment of a screen image acquisition viewing angle unevenness correction device, comprising:

[0127] Setting unit 801 is used to set a backlight in the placement area of ​​the imaging system when the imaging system is in an offline stage.

[0128] The adjustment unit 802 is used to adjust the light source of the imaging system so that the optical conditions of the imaging system are in line with the online stage.

[0129] The first generation unit 803 is used to turn on the backlight, acquire images of the backlight of the imaging system, and generate a backlight image.

[0130] The second generation unit 804 is used to perform flat field correction processing on the backlight image to generate a backlight correction coefficient image.

[0131] Optionally, the second generation unit 804 includes:

[0132] Bright-field and dark-field images are acquired using an imaging system.

[0133] The backlight image is flat-field corrected based on the bright-field and dark-field images to generate a backlight correction coefficient image.

[0134] The acquisition unit 805 is used to adjust the imaging system to the online stage, place the screen under test on the backlight board, and acquire the image of the screen under test under different display gray levels.

[0135] The preprocessing unit 806 is used to perform preprocessing operations on the image to be corrected, including coordinate transformation, geometric correction, Gaussian filtering, and edge correction processing.

[0136] The first acquisition unit 807 is used to acquire the maximum and minimum gray levels in the image to be corrected, and to acquire the folding curvature parameters of the screen to be tested and the reflectivity parameters of the microcircuit region under the folding curvature parameters.

[0137] The fourth generation unit 808 is used to generate a contrast adjustment formula based on the maximum gray level, minimum gray level, fold curvature parameter and reflectivity parameter.

[0138] The enhancement unit 809 is used to perform visual contrast enhancement processing on the grayscale data of the image to be corrected according to the contrast adjustment formula.

[0139] The correction unit 810 is used to correct the image to be corrected once using the backlight correction coefficient image.

[0140] The first building unit 811 is used to build a global brightness trend surface image of the image to be corrected after one correction by means of a surface fitting method.

[0141] The third generation unit 812 is used to generate viewing angle unevenness correction coefficients from the image pixel values ​​in the global brightness trend surface image.

[0142] Optionally, the third generation unit 812 includes:

[0143] The image pixel values ​​in the global brightness trend surface image are subjected to centering normalization and inverse calculation to generate the viewing angle unevenness correction coefficient.

[0144] The correction unit 813 is used to perform viewpoint unevenness correction on the image to be corrected after one correction by using the viewpoint unevenness correction coefficient.

[0145] The second acquisition unit 814 is used to acquire global brightness trend surface images and viewing angle unevenness correction coefficients of several screens under test.

[0146] The second building unit 815 is used to perform statistical modeling and parameter fusion based on the global brightness trend surface image and the viewpoint unevenness correction coefficient to build a representative standardized correction coefficient model.

[0147] The auxiliary correction unit 816 is used to input the standardized correction coefficient model into the process of uneven view correction for auxiliary correction.

[0148] Please see Figure 9 This application provides a viewing angle unevenness correction device for screen image acquisition, comprising:

[0149] Processor 901, memory 902, input / output unit 903, and bus 904.

[0150] The processor 901 is connected to the memory 902, the input / output unit 903, and the bus 904.

[0151] The memory 902 stores a program, and the processor 901 calls the program to execute it, such as... Figure 1 , Figure 2 and Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 Methods for correcting uneven viewing angles in [the context of the text].

[0152] This application provides a computer-readable storage medium on which a program is stored, and when the program is executed on a computer, it performs the following... Figure 1 , Figure 2 and Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 Methods for correcting uneven viewing angles in [the context of the text].

[0153] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0154] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0155] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0156] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0157] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for correcting viewing angle unevenness in screen image acquisition, characterized in that, include: When the imaging system is in an offline stage, a backlight is set in the placement area of ​​the imaging system; Turn on the backlight, acquire an image of the backlight of the imaging system, and generate a backlight image. The backlight image is subjected to flat field correction processing to generate a backlight correction coefficient image; The step of performing flat field correction processing on the backlight image to generate a backlight correction coefficient image includes: acquiring a bright field image and a dark field image through the imaging system; performing flat field correction processing on the backlight image based on the bright field image and the dark field image to generate a backlight correction coefficient image; The imaging system is set to online mode, the screen under test is placed on the backlight, and images of the screen under test to be calibrated are acquired at different display gray levels. The image to be corrected is corrected once using the backlight image; A global brightness trend surface image of the image to be corrected after one correction is constructed using the surface fitting method; Generate viewing angle unevenness correction coefficients from the image pixel values ​​in the global brightness trend surface image; The step of generating a viewing angle unevenness correction coefficient from the image pixel values ​​in the global brightness trend surface image includes: performing centering normalization processing and reverse calculation processing on the image pixel values ​​in the global brightness trend surface image to generate a viewing angle unevenness correction coefficient. The uneven viewpoint correction coefficient is used to correct the uneven viewpoint of the image to be corrected after one correction. Obtain global brightness trend surface images and viewing angle unevenness correction coefficients for several screens under test; Based on the global brightness trend surface image and the viewing angle unevenness correction coefficient, statistical modeling and parameter fusion are performed to construct a representative standardized correction coefficient model. The standardized correction coefficient model is used as an auxiliary correction in the process of uneven viewpoint correction.

2. The method for correcting uneven viewing angles according to claim 1, characterized in that, When the imaging system is in an offline stage, after the step of setting a backlight in the placement area of ​​the imaging system and before the step of turning on the backlight, acquiring an image of the backlight of the imaging system, and generating a backlight image, the viewing angle unevenness correction method further includes: Adjust the light source of the imaging system so that the optical conditions of the imaging system are the same as those in the online stage.

3. The method for correcting uneven viewing angles according to any one of claims 1 to 2, characterized in that, The screen under test is a foldable screen with a microcircuit region located in the foldable area of ​​the foldable screen. The screen under test is folded to a preset degree during the acquisition of the image to be corrected. After the steps of adjusting the imaging system to online mode, placing the screen under test on the backlight panel, and acquiring images of the screen under test at different display grayscale levels, and before the step of correcting the image under test using the backlight image, the viewing angle unevenness correction method further includes: The maximum and minimum gray levels in the image to be corrected are obtained, and the folding curvature parameters of the screen to be tested and the reflectivity parameters of the microcircuit region under the folding curvature parameters are obtained. A contrast adjustment formula is generated based on the maximum gray level, the minimum gray level, the fold curvature parameter, and the reflectivity parameter; The grayscale data of the image to be corrected is subjected to visual contrast enhancement processing according to the contrast adjustment formula.

4. The method for correcting uneven viewing angles according to any one of claims 1 to 2, characterized in that, After the steps of adjusting the imaging system to online mode, placing the screen under test on the backlight panel, and acquiring images of the screen under test at different display grayscale levels, and before the step of correcting the image under test using the backlight image, the viewing angle unevenness correction method further includes: The image to be corrected is subjected to preprocessing operations, including coordinate transformation, geometric correction, Gaussian filtering, and edge correction.

5. A device for correcting uneven viewing angles in screen image acquisition, characterized in that, include: The setting unit is used to set a backlight in the placement area of ​​the imaging system when the imaging system is in an offline stage. The first generation unit is used to turn on the backlight, acquire images of the backlight of the imaging system, and generate a backlight image. The second generation unit is used to perform flat field correction processing on the backlight image to generate a backlight correction coefficient image. The second generation unit includes: acquiring bright-field images and dark-field images through an imaging system; performing flat-field correction processing on the backlight image based on the bright-field images and dark-field images to generate a backlight correction coefficient image; The acquisition unit is used to adjust the imaging system to the online stage, place the screen under test on the backlight, and acquire the image of the screen under test under different display gray levels. The correction unit is used to correct the image to be corrected once using the backlight image; The first construction unit is used to construct a global brightness trend surface image of the image to be corrected after one correction by means of a surface fitting method; The third generation unit is used to generate a viewing angle unevenness correction coefficient from the image pixel values ​​in the global brightness trend surface image. The third generation unit includes: performing centering normalization and reverse calculation on the image pixel values ​​in the global brightness trend surface image to generate viewing angle unevenness correction coefficients. The correction unit is used to perform viewpoint unevenness correction on the image to be corrected after one correction using the viewpoint unevenness correction coefficient; The second acquisition unit is used to acquire global brightness trend surface images and viewing angle unevenness correction coefficients of several screens under test. The second building unit is used to perform statistical modeling and parameter fusion based on the global brightness trend surface image and the viewpoint unevenness correction coefficient to build a representative standardized correction coefficient model. The auxiliary correction unit is used to assist in the correction of uneven viewing angles by incorporating the standardized correction coefficient model into the process.

6. The viewing angle unevenness correction device according to claim 5, characterized in that, After the setting unit and before the first generating unit, the viewing angle unevenness correction device further includes: An adjustment unit is used to adjust the light source of the imaging system so that the optical conditions of the imaging system are the same as those in the online stage.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains a program that, when executed on a computer, performs the viewpoint unevenness correction method as described in any one of claims 1 to 4.

Images