Ghost image elimination method and device, electronic equipment, storage medium and program product

Abstract

The invention relates to a ghost image elimination method and device, electronic equipment, a storage medium and a program product, and the method comprises the steps: obtaining an original input image and a current eye point position, and the original input image comprises a main image and a background; determining a plurality of ghost image positions corresponding to the main image and the brightness color of each ghost image position according to the current eye point position, and determining a brightness compensation coefficient or a first compensation matrix of the original input image according to the brightness color of each ghost image position and the brightness colors of all positions of the background; and compensating the original input image according to the brightness compensation coefficient or the first compensation matrix to obtain a ghost image compensation graph of the original input image. Therefore, the problems that all ghost images cannot be completely eliminated and the process improvement process is tedious in the prior art are solved, the ghost images are eliminated in the mode that the background brightness and color are changed through the image algorithm under the condition that the optical design and the optical process are not changed, and therefore the display quality and the user experience are improved.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to a method, apparatus, electronic device, storage medium, and program product for ghost image removal. Background Technology

[0002] Near-eye displays (NEDs), as an emerging display technology, have been widely used in fields such as virtual reality (VR) and augmented reality (AR). These devices typically employ a pancake optical solution, favored for its thinness and compactness. However, due to multiple reflections in the light path, pancake optical systems often produce ghosting (extra virtual images formed outside the main image). These ghosting images can negatively impact the user's visual experience and reduce display quality.

[0003] In related technologies, ghosting is generally eliminated or improved by improving optical design and manufacturing processes, such as by attaching special films or applying coatings to the surface of optical elements to reduce reflection.

[0004] However, this method has certain limitations; it cannot completely eliminate all ghost images, and the process of improving the technique is quite complicated, which urgently needs to be addressed. Summary of the Invention

[0005] This application provides a ghost image removal method, apparatus, electronic device, storage medium, and program product to solve the problems that related technologies cannot completely eliminate all ghost images and that the process improvement is cumbersome. Without changing the optical design and optical process, ghost images are eliminated by using image algorithms to change the background brightness and color, thereby improving display quality and user experience.

[0006] The first aspect of this application provides a method for eliminating ghost images, including the following steps:

[0007] Obtain the original input image and the current eye point position, wherein the original input image includes the main image and the background;

[0008] Based on the current eye point position, determine the multiple ghost image positions corresponding to the main image and the brightness and color of each ghost image position, and determine the brightness compensation coefficient or first compensation matrix of the original input image based on the brightness and color of each ghost image position and the brightness and color of all positions of the background.

[0009] The original input image is compensated according to the brightness compensation coefficient or the first compensation matrix to obtain the ghost image compensation map of the original input image.

[0010] According to one embodiment of this application, determining the brightness compensation coefficient or first compensation matrix of the original input image based on the brightness and color of each ghost image location and the brightness and color of all locations in the background includes:

[0011] When the colors of the multiple ghost images are consistent with the background color, a target brightness for ghost image compensation is determined, and a brightness compensation coefficient for each position other than the main image is calculated based on the target brightness.

[0012] According to one embodiment of this application, the step of compensating the original input image according to the brightness compensation coefficient to obtain a ghost image compensation map of the original input image includes:

[0013] The original input image is converted from a non-linear color space to a linear color space to obtain the linear space brightness of each position in the original input image;

[0014] The compensated original input image is obtained based on the linear spatial brightness of each position of the original input image, the brightness compensation coefficient of each position of the main image, and the brightness compensation coefficient of each position other than the main image, wherein the brightness compensation coefficient of each position of the main image is 1.

[0015] The compensated original input image is converted from the linear color space to the nonlinear color space to obtain the ghost image compensation map.

[0016] According to one embodiment of this application, determining the target brightness for ghost image compensation includes:

[0017] The target brightness is obtained by summing the brightness of the brightest ghost image among the plurality of ghost images and the background brightness at the corresponding position of the ghost image.

[0018] According to one embodiment of this application, determining the brightness compensation coefficient or first compensation matrix of the original input image based on the brightness and color of each ghost image position and the brightness and color of all positions of the background further includes:

[0019] When the colors of the multiple ghost images are inconsistent with the color of the background, the target background coefficient is calculated based on the brightness color of the ghost image with the highest brightness among the multiple ghost images and the brightness color of the background at the corresponding position of the ghost image;

[0020] Calculate the target brightness color at each position of the background based on the target background coefficient, and calculate the first compensation matrix for each position other than the main image based on the target brightness color at each position of the background.

[0021] According to one embodiment of this application, the step of compensating the original input image based on the first compensation matrix to obtain a ghost image compensation map of the original input image includes:

[0022] The original input image is converted from a non-linear color space to a linear color space to obtain the linear space brightness and color of each position in the original input image;

[0023] The compensated original input image is obtained based on the linear spatial brightness and color of each position of the original input image, the first compensation matrix of each position of the main image, and the first compensation matrix of each position other than the main image, wherein the first compensation matrix of each position of the main image is 1;

[0024] The compensated original input image is converted from the linear color space to the nonlinear color space to obtain the ghost image compensation map.

[0025] According to one embodiment of this application, a target background coefficient is calculated based on the brightness and color of the ghost image with the highest brightness among the plurality of ghost images and the brightness and color of the background at the corresponding position of the ghost image, including:

[0026] Based on the brightness and color of the ghost image with the highest brightness among the multiple ghost images and the brightness and color of the background at the corresponding position of the ghost image, the background coefficients corresponding to the multiple second compensation matrices are determined, and multiple background coefficients are obtained.

[0027] The minimum background coefficient among the plurality of background coefficients is taken as the target background coefficient.

[0028] According to one embodiment of this application, determining the positions of multiple ghost images corresponding to the main image and the brightness and color of each ghost image position based on the current eye point position includes:

[0029] Based on the current eye point position, obtain multiple ghost positions corresponding to the main image and the brightness and color of each ghost position from the preset ghost image LUT (Look Up Table).

[0030] According to one embodiment of this application, before obtaining the multiple ghost image positions corresponding to the main image and the brightness and color of each ghost image position from the preset ghost image LUT based on the current eye point position, the method further includes:

[0031] Determine the three-dimensional spatial region in the display device under test that corresponds to the user's eye position;

[0032] Determine multiple positions to be lit on the display screen of the display device under test and the order of each position to be lit;

[0033] Based on the order of each position to be lit, multiple solid color images are used to light up the current position to be lit in sequence, and the three-dimensional space region is traversed to obtain multiple target ghost positions and the brightness and color of each target ghost position corresponding to the current position to be lit at each eye point position.

[0034] The preset ghost image LUT is generated based on the multiple target ghost image positions corresponding to the current position to be lit at each eye point position and the brightness and color of each target ghost image position.

[0035] According to the ghost image removal method provided in this application, multiple ghost image positions corresponding to the main image and the brightness and color of each ghost image position are determined based on the current eye point position. A brightness compensation coefficient or a first compensation matrix for the original input image is also determined. The original input image is then compensated based on the brightness compensation coefficient or the first compensation matrix to obtain a ghost image compensation map of the original input image. This solves the problem that related technologies cannot completely eliminate all ghost images, and the process of improving the technology is cumbersome. Without changing the optical design and optical process, ghost images are eliminated by using image algorithms to change the background brightness and color, thereby improving display quality and user experience.

[0036] A second aspect of this application provides a ghost image removal device, comprising:

[0037] The acquisition module is used to acquire the original input image and the current eye point position, wherein the original input image includes the main image and the background;

[0038] The determination module is used to determine multiple ghost image positions corresponding to the main image and the brightness and color of each ghost image position based on the current eye point position, and to determine the brightness compensation coefficient or first compensation matrix of the original input image based on the brightness and color of each ghost image position and the brightness and color of all positions of the background.

[0039] The image compensation module is used to compensate the original input image according to the brightness compensation coefficient or the first compensation matrix to obtain the ghost image compensation map of the original input image.

[0040] According to one embodiment of this application, the determining module is configured to:

[0041] When the colors of the multiple ghost images are consistent with the background color, a target brightness for ghost image compensation is determined, and a brightness compensation coefficient for each position other than the main image is calculated based on the target brightness.

[0042] According to one embodiment of this application, the image compensation module is used for:

[0043] The original input image is converted from a non-linear color space to a linear color space to obtain the linear space brightness of each position in the original input image;

[0044] The compensated original input image is obtained based on the linear spatial brightness of each position of the original input image, the brightness compensation coefficient of each position of the main image, and the brightness compensation coefficient of each position other than the main image, wherein the brightness compensation coefficient of each position of the main image is 1.

[0045] The compensated original input image is converted from the linear color space to the nonlinear color space to obtain the ghost image compensation map.

[0046] According to one embodiment of this application, the determining module is configured to:

[0047] The target brightness is obtained by summing the brightness of the brightest ghost image among the plurality of ghost images and the background brightness at the corresponding position of the ghost image.

[0048] According to one embodiment of this application, the determining module is further configured to:

[0049] When the colors of the multiple ghost images are inconsistent with the color of the background, the target background coefficient is calculated based on the brightness color of the ghost image with the highest brightness among the multiple ghost images and the brightness color of the background at the corresponding position of the ghost image;

[0050] Calculate the target brightness color at each position of the background based on the target background coefficient, and calculate the first compensation matrix for each position other than the main image based on the target brightness color at each position of the background.

[0051] According to one embodiment of this application, the image compensation module is used for:

[0052] The original input image is converted from a non-linear color space to a linear color space to obtain the linear space brightness and color of each position in the original input image;

[0053] The compensated original input image is obtained based on the linear spatial brightness and color of each position of the original input image, the first compensation matrix of each position of the main image, and the first compensation matrix of each position other than the main image, wherein the first compensation matrix of each position of the main image is 1;

[0054] The compensated original input image is converted from the linear color space to the nonlinear color space to obtain the ghost image compensation map.

[0055] According to one embodiment of this application, the determining module is configured to:

[0056] Based on the brightness and color of the ghost image with the highest brightness among the multiple ghost images and the brightness and color of the background at the corresponding position of the ghost image, the background coefficients corresponding to the multiple second compensation matrices are determined, and multiple background coefficients are obtained.

[0057] The minimum background coefficient among the plurality of background coefficients is taken as the target background coefficient.

[0058] According to one embodiment of this application, the determining module is configured to:

[0059] Based on the current eye point position, obtain multiple ghost positions corresponding to the main image and the brightness and color of each ghost position from the preset ghost image LUT.

[0060] According to one embodiment of this application, before obtaining multiple ghost image positions corresponding to the main image and the brightness and color of each ghost image position from the preset ghost image LUT based on the current eye point position, the determining module is further configured to:

[0061] Determine the three-dimensional spatial region in the display device under test that corresponds to the user's eye position;

[0062] Determine multiple positions to be lit on the display screen of the display device under test and the order of each position to be lit;

[0063] Based on the order of each position to be lit, multiple solid color images are used to light up the current position to be lit in sequence, and the three-dimensional space region is traversed to obtain multiple target ghost positions and the brightness and color of each target ghost position corresponding to the current position to be lit at each eye point position.

[0064] The preset ghost image LUT is generated based on the multiple target ghost image positions corresponding to the current position to be lit at each eye point position and the brightness and color of each target ghost image position.

[0065] According to the ghost image elimination device provided in this application embodiment, multiple ghost image positions corresponding to the main image and the brightness and color of each ghost image position are determined based on the current eye point position. A brightness compensation coefficient or a first compensation matrix of the original input image is also determined. The original input image is then compensated based on the brightness compensation coefficient or the first compensation matrix to obtain a ghost image compensation map of the original input image. This solves the problem that related technologies cannot completely eliminate all ghost images, and the process improvement is cumbersome. Without changing the optical design and optical process, ghost images are eliminated by using image algorithms to change the background brightness and color, thereby improving display quality and user experience.

[0066] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the ghost image removal method as described in the above embodiments.

[0067] A fourth aspect of this application provides a computer-readable storage medium storing computer instructions for causing the computer to perform the ghost removal method as described in the above embodiments.

[0068] A fourth aspect of this application provides a computer program product, including a computer program that is executed to implement the ghost image removal method as described in the above embodiments.

[0069] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0070] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0071] Figure 1 This is a flowchart illustrating the process of a ghost removal system according to an embodiment of this application;

[0072] Figure 2 This is a flowchart of a ghost image removal method provided according to an embodiment of this application;

[0073] Figure 3 This is a schematic diagram of 10*10 pixels at position (i, j) of the DUT display screen according to an embodiment of this application;

[0074] Figure 4 A flowchart illustrating the generation of a preset ghost image LUT according to an embodiment of this application;

[0075] Figure 5 This is a schematic diagram illustrating the compensation effect of eliminating ghosting by increasing background brightness according to an embodiment of this application;

[0076] Figure 6 This is a flowchart of a ghost image removal method according to an embodiment of this application, where the ghost image brightness and color are the same as the background brightness and color.

[0077] Figure 7 This is a schematic diagram illustrating the compensation effect of eliminating ghost images by changing the background color according to an embodiment of this application;

[0078] Figure 8This is a flowchart of a ghost image removal method according to an embodiment of the present application when the brightness and color of the ghost image are inconsistent with the brightness and color of the background;

[0079] Figure 9 This is a block diagram of a ghost removal device according to an embodiment of this application;

[0080] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0081] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0082] The ghost removal method, apparatus, electronic device, storage medium, and program product of this application are described below with reference to the accompanying drawings.

[0083] Before introducing the ghost removal method of the embodiments of this application, let's briefly introduce the ghost removal methods in related technologies.

[0084] A method for eliminating ghost images is proposed in related technologies. This method can be used in optical imaging systems (fundus cameras) for ophthalmic diagnosis and examination. It extracts two-dimensional eye image information that is obscured by ghost images from two images and then stitches them together to eliminate the ghost images and obtain a complete image.

[0085] The related technology also proposes a system and method for eliminating ghost images in under-screen imaging. The method involves removing ghost images from a second image based on the location of the ghost image region to obtain a fourth image, and filling the ghost image region in the fourth image with the non-ghost image region to obtain a fifth image. In addition, the method involves detecting the true contour image of the target object in the third image and fusing the true contour image of the target object with the fifth image to obtain a ghost image-free image.

[0086] The related technology also proposes a method for eliminating ghosting in near-eye display devices. Through a special design of the optical path, ghosting in near-eye display devices can be effectively prevented without affecting luminous efficiency.

[0087] The related technology also proposes an optical imaging method for eliminating ghosting in the field of camera shooting, which eliminates ghosting by adjusting the angle of the planar optical device at the front end of the camera.

[0088] Therefore, it can be seen that in related technologies, when improving or eliminating ghosting, the improvement is generally achieved by improving the optical design and process. For example, by attaching special films or coating the surface of optical elements to reduce reflection. However, this method has certain limitations, cannot completely eliminate all ghosting, and the process of improving the process is relatively complicated.

[0089] Based on the above problems, this application proposes a ghost image removal method. This method can determine multiple ghost image positions corresponding to the main image and the brightness and color of each ghost image position according to the current eye point position, and determine the brightness compensation coefficient or first compensation matrix of the original input image. The original input image is then compensated according to the brightness compensation coefficient or first compensation matrix to obtain the ghost image compensation map of the original input image. Thus, without changing the optical design and optical process, ghost images are eliminated by using image algorithms to change the background brightness and color, thereby improving display quality and user experience.

[0090] Let's combine the following... Figure 1 This application introduces the ghost removal system involved in the ghost removal method of the embodiment of the present application.

[0091] like Figure 1 As shown, the ghost image removal system mainly includes a ghost image calibration module and a ghost image compensation module. The ghost image calibration module is used to obtain a LUT composed of the user's eye position, color, and grayscale. The ghost image compensation module compensates for the input image displayed near the eye, thereby eliminating ghost images.

[0092] Next, the ghost image removal method of the embodiments of this application will be described in detail.

[0093] Specifically, Figure 2 This is a schematic flowchart of a ghost image removal method provided in an embodiment of this application.

[0094] like Figure 2 As shown, the method for eliminating ghost images includes the following steps:

[0095] In step S201, the original input image and the current eye point position are obtained, wherein the original input image includes the main image and the background.

[0096] Here, the raw input image refers to image data without any processing, that is, the image sent directly from the image source to the display device. The current eye point position refers to the position of the user's eye relative to the effective field of view (eye box) of the near-eye display device.

[0097] Optionally, embodiments of this application can directly obtain the original input image from the image source. Embodiments of this application can also use built-in sensors (such as infrared sensors, cameras, etc.) to track the position of the user's eyes, thereby obtaining the current eye point position, without specific limitations.

[0098] In step S202, multiple ghost image positions corresponding to the main image and the brightness and color of each ghost image position are determined according to the current eye point position, and the brightness compensation coefficient or first compensation matrix of the original input image is determined according to the brightness and color of each ghost image position and the brightness and color of all positions in the background.

[0099] In some embodiments, determining the multiple ghost image positions corresponding to the main image and the brightness and color of each ghost image position based on the current eye position includes: obtaining the multiple ghost image positions corresponding to the main image and the brightness and color of each ghost image position from a preset ghost image LUT based on the current eye position.

[0100] Specifically, the LUT stores the positions (i,j) of multiple ghost images that may appear at the current eye position, as well as the brightness and color [X_Gh,Y_Gh,Z_Gh] of each ghost image. In this embodiment, the current eye position (m,n,l) can be used as an index to look up the corresponding data in the preset ghost image LUT, thereby obtaining the positions of multiple ghost images corresponding to the main image position and the brightness and color of each ghost image position. The calibration of the preset ghost image LUT will be described in detail later.

[0101] It should be noted that there are many ways to determine the brightness compensation coefficient or the first compensation matrix of the original input image, which will be explained in detail below with reference to specific embodiments.

[0102] First, this application will explain how to determine the brightness compensation coefficient of the original input image in the embodiments.

[0103] In some embodiments, the brightness compensation coefficient of the original input image is determined based on the brightness and color of each ghost image location and the brightness and color of all locations in the background. This includes: when the colors of multiple ghost images and the background are consistent, determining the target brightness for ghost image compensation, and calculating the brightness compensation coefficient for each location except the main image based on the target brightness.

[0104] In some embodiments, determining the target brightness for ghost image compensation includes: obtaining the target brightness based on the sum of the brightness of the ghost image with the highest brightness among multiple ghost images and the background brightness at the corresponding position of the ghost image.

[0105] It should be understood that brightness and color are generally represented by three-dimensional coordinates [X_Gh, Y_Gh, Z_Gh]. Color is determined by [X_Gh, Y_Gh, Z_Gh], while brightness is generally determined only by Y_Gh. Therefore, if the colors of multiple ghost images are the same as the background color, this embodiment only requires brightness compensation. Thus, this embodiment can obtain multiple ghost image positions (i, j) and the corresponding brightness γ_Gh of each ghost image position from the original input image using a preset ghost image LUT. ijThen, the target brightness is obtained by summing the brightness of the brightest ghost image among multiple ghost images and the background brightness at the corresponding position of that ghost image.

[0106] For example, if the position of the brightest ghost image among multiple ghost images is (2,1), then the brightness of the brightest ghost image among multiple ghost images is Max(Y_Gh). 21 The background brightness at the corresponding location of the ghost image is Y. 21 The target brightness is Max(Y_Gh) 21 )+Y 21 The sum of these values, i.e., the target brightness Y_goal = Max(Y_Gh) 21 )+Y 21 .

[0107] Therefore, after determining the target brightness, the embodiments of this application can calculate the brightness compensation coefficient Q for each position other than the main image based on the target brightness. ij Make the brightness at every position except the main image satisfy Y_goal = Q ij *Y ij +Y_Gh ij Among them, Y_Gh ij ≥0.

[0108] Furthermore, embodiments of this application illustrate how to determine the first compensation matrix of the original input image.

[0109] In some embodiments, when determining whether the colors of multiple ghost images are consistent with the background color, the method further includes: if the colors of multiple ghost images are inconsistent with the background color, calculating a target background coefficient based on the brightness color of the ghost image with the highest brightness among the multiple ghost images and the brightness color of the background at the corresponding position of that ghost image; calculating a target brightness color for each position of the background based on the target background coefficient; and calculating a first compensation matrix involving each position except the main image based on the target brightness color of each position of the background. The first compensation matrix is ​​a matrix used to perform brightness and color compensation for each position except the main image.

[0110] In some embodiments, calculating a target background coefficient based on the brightness and color of the brightest ghost image among multiple ghost images and the brightness and color of the background at the corresponding position of that ghost image includes: determining background coefficients corresponding to multiple second compensation matrices based on the brightness and color of the brightest ghost image among multiple ghost images and the brightness and color of the background at the corresponding position of that ghost image, thus obtaining multiple background coefficients; and taking the smallest background coefficient among the multiple background coefficients as the target background coefficient. The second compensation matrix is ​​a matrix used to compensate for the brightness and color of the position of the brightest ghost image among multiple ghost images.

[0111] It should be understood that since the color is determined by [X_Gh, Y_Gh, Z_Gh], when the colors of multiple ghost images are inconsistent with the background color, in order to ensure that the brightness and color of the ghost images are consistent with the brightness and color of the background after compensation, this embodiment of the application needs to perform compensation through the three-dimensional coordinates corresponding to the brightness and color.

[0112] Specifically, in this embodiment of the application, multiple ghost image positions (i,j) of the original input image and the brightness and color [X_Gh] corresponding to each ghost image position can be obtained through a preset ghost image LUT. ij ,Y_Gh ij ,Z_Gh ij The algorithm calculates the brightness and color of all locations in the background [Xij, Yij, Zij] based on the original input image. If the brightness and color of the ghost image do not match the brightness and color of the background, the algorithm calculates the brightness and color of the ghost image with the highest brightness among the multiple ghost images, using Max([X_Gh...). ij ,Y_Gh ij ,Z_Gh ij ]) and the background brightness and color at the corresponding position of the ghost image [X ij ,Y ij Z ij Calculate the second compensation matrix P ij And the background coefficient N, such that the position of the ghost image with the maximum brightness satisfies N*[X ij ,Y ij Z ij ] = P ij *[X ij ,Y ij Z ij ]+Max[X_Gh ij ,Y_Gh ij ,Z_Gh ij In the XYZ color space, X represents the spectral composition from red to blue, Y represents the luminance component, and Z represents the spectral composition from green to blue. The XYZ color space is device-independent, meaning it can be used to convert colors between different devices and media while maintaining color consistency. This is achieved by ensuring that the position of the ghost image with maximum luminance satisfies N*[X...]. ij ,Y ij Z ij ] = P ij *[X ij ,Y ij Z ij ]+Max[X_Gh ij ,Y_Gh ij ,Z_Gh ij This allows the brightness and color of the most luminous ghost image to be matched or offset by adjusting the background brightness and color, thereby eliminating the ghost image.

[0113] For example, if the brightest ghost image among multiple ghost images is located at (2,1), then the brightness color of the brightest ghost image among multiple ghost images is Max([X_Gh 21 ,Y_Gh 21 ,Z_Gh 21 The background brightness color corresponding to the location of the brightest ghost image is [X]. 21 ,Y 21 Z 21 ], then the position of the ghost image with the highest brightness must satisfy N*[X 21 ,Y 21 Z 21 ] = P 21 *[X 21 ,Y 21 Z 21 ]+Max([X_Gh 21 ,Y_Gh 21 ,Z_Gh 21 Thus, the background coefficients corresponding to multiple second compensation matrices can be calculated. That is, given a second compensation matrix, a background coefficient can be calculated. Since the compensation effect is best when the background coefficient is the smallest, the embodiment of this application can select the smallest value as the target background coefficient from the background coefficients corresponding to multiple second compensation matrices.

[0114] Furthermore, after calculating the target background coefficient, this application can also calculate the first compensation matrix for each position other than the main image based on the target background coefficient.

[0115] As one possible implementation, embodiments of this application can calculate the target brightness color at each location in the background based on the target background coefficient. Assuming the location of the brightest ghost image among multiple ghost images is (2,1), then the target brightness color Goal = N*[X] 21 ,Y 21 Z 21 Finally, the embodiments of this application can be based on the following formula: Goal = M ij *[X ij ,Y ij Z ij ]+[X_Gh ij ,Y_Gh ij ,Z_Gh ij ] Calculate the first compensation matrix for each position other than the main image. That is, each position other than the main image will have a corresponding first compensation matrix, thereby achieving the effect of eliminating ghost images by adjusting brightness and color.

[0116] To facilitate understanding, the calibration process of the preset ghost image LUT will be explained in detail below.

[0117] As one possible implementation, in some embodiments, before obtaining multiple ghost image positions corresponding to the main image and the brightness and color of each ghost image position from a preset ghost image LUT based on the current eye position, the method further includes: determining a three-dimensional spatial region in the display device under test corresponding to the user's eye position; determining multiple positions to be lit on the display screen of the display device under test and the order of each position to be lit; based on the order of each position to be lit, sequentially using multiple solid color images to light up the current position to be lit, and traversing the three-dimensional spatial region to obtain multiple target ghost image positions corresponding to the current position to be lit at each eye position and the brightness and color of each target ghost image position; and generating a preset ghost image LUT based on the multiple target ghost image positions corresponding to the current position to be lit at each eye position and the brightness and color of each target ghost image position.

[0118] Among them, the display device to be tested can be a near-eye display device.

[0119] The calibration process of the preset ghost image LUT in some embodiments is illustrated in detail below.

[0120] First, the camera's shooting position is located within the three-dimensional area (m, n, l) of the DUT's (Device Under Test) eye box, with parameter ranges of -5mm ≤ m < 5mm, -5mm ≤ n < 5mm, and -5mm ≤ l < 5mm. Of course, the specific values ​​of m, n, and l can be limited according to actual needs; this is only an example selection.

[0121] The DUT eyebox 3D region refers to a 3D spatial region related to the user's eye. This region defines the range within which the user's eye can move in the near-eye display device. When the user's eye moves within this region, they can still see a complete image without significant distortion or quality degradation. The camera's position parameters m, n, and l represent coordinates along the three axes, ranging from -5mm to 5mm.

[0122] Secondly, such as Figure 3 As shown, 10*10 pixels at position (i, j) of the DUT display are illuminated sequentially as the main image. For each position, four pure color images (R (red), G (green), B (blue), and W (white)) need to be switched sequentially to ensure coverage of all possible color combinations, thus comprehensively identifying ghost images. Similarly, the 10*10 pixel size here is only an example; the specific pixel values ​​for the main image can be other values, which are not limited here.

[0123] Next, the test results of the DUT are obtained using the image acquisition module, including the number of ghost images n (the number of ghost images ≥ 0), the brightness and color of the ghost images [X_Gh,Y_Gh,Z_Gh], and their positions (i,j) mapped to the display screen.

[0124] Finally, as the camera traverses the 5*5*5 three-dimensional space range along the m, n, and l axes within the eye box, the positions of multiple target ghost images corresponding to the current position to be illuminated at each eye point position and the brightness and color of each target ghost image position are obtained. Based on the multiple target ghost image positions corresponding to the current position to be illuminated at each eye point position and the brightness and color of each target ghost image position, a preset ghost image LUT is generated, as shown in Table 1. Table 1 is the preset ghost image LUT table.

[0125] Table 1

[0126]

[0127] To facilitate understanding by those skilled in the art regarding the generation process of the preset ghost image LUT in the embodiments of this application, the following is combined with... Figure 4 Please provide a detailed explanation.

[0128] like Figure 4 As shown, the generation process of this preset ghost image LUT includes the following steps:

[0129] S401, Begin.

[0130] S402, the camera shooting position is within the DUT eyebox area (m,n,l), -5≤m<5, -5≤n<5, -5≤n<5.

[0131] S403, illuminate 10*10 pixels at position (i,j) on the DUT display screen as the main image a. ij Each position requires switching between the R, G, B, and W solid color images in sequence.

[0132] S404, the image acquisition module obtains the ghost image test results, including the number of ghost images (n≥0), brightness and color [X_Gh ij ,Y_Gh ij ,Z_Gh ij [and the position (i,j) mapped to the display screen (number of ghost images ≥ 0).]

[0133] S405, determine whether m, n, l have already traversed the range [-5, 5]. If so, execute S406; otherwise, execute S402.

[0134] S406, obtain the LUT for the ghost image.

[0135] In step S203, the original input image is compensated according to the brightness compensation coefficient or the first compensation matrix to obtain the ghost image compensation map of the original input image.

[0136] In some embodiments, the original input image is compensated according to a brightness compensation coefficient to obtain a ghost image compensation map of the original input image. This includes: converting the original input image from a nonlinear color space to a linear color space to obtain the linear space brightness of each position in the original input image; obtaining the compensated original input image based on the linear space brightness of each position in the original input image, the brightness compensation coefficient of each position in the main image, and the brightness compensation coefficient of each position other than the main image, wherein the brightness compensation coefficient of each position in the main image is 1; and converting the compensated original input image from a linear color space to a nonlinear color space to obtain the ghost image compensation map.

[0137] Specifically, to ensure the accuracy of brightness changes, a linear color space is required during brightness compensation. Therefore, degamma processing is performed on the original input image to convert it from a non-linear color space to a linear color space, i.e., grayscale. ij Convert to linear spatial brightness Y ij .

[0138] Further, the compensated input image brightness Y is calculated. ij_comp For each location in the background, a brightness compensation coefficient is used to calculate the compensated brightness Y. ij_comp =M ij *Y ij For the main image, the brightness compensation coefficient M of the main image ij The brightness Y of the compensated input image is 1. ij_comp =Y ij That is, the brightness of the main image remains unchanged.

[0139] Furthermore, the compensated input image is converted from a linear color space back to a non-linear color space, i.e., regamma processing is performed on the compensated input image, and the compensated input brightness Y... ij_comp Transformation to nonlinear space Grey ij_comp This yields a ghost image compensation map, where the obtained ghost image pre-compensation effect is as follows: Figure 5 As shown.

[0140] To facilitate understanding by those skilled in the art of completing the ghost image compensation map when the ghost image brightness and color are consistent with the background brightness and color in the embodiments of this application, the following is combined with... Figure 6 Please provide a detailed explanation.

[0141] like Figure 6 As shown, when the brightness and color of the ghost image are the same as the brightness and color of the background, the process of completing the ghost image compensation map includes the following steps:

[0142] S601, Begin.

[0143] S602, determine the original input image and eye point position.

[0144] S603, obtain the positions (i,j) and corresponding brightness Y_Gh of multiple ghost images in the input image through LUT. ij .

[0145] S604, obtain the brightness Y of all locations in the background based on the input image. ij .

[0146] S605, the brightest ghost image among multiple ghost images, Max(Y_Gh) ij The target brightness Max(Y_Gh) for ghost image compensation is calculated using the background brightness value Yij corresponding to that location and the corresponding background brightness value Yij. ij )+Y ij .

[0147] S606, Calculate the brightness compensation coefficient M ij Make the brightness of every position except the main image satisfy Max(Y_Gh) ij )+Y ij =M ij *Y ij +Y_Gh ij Y_Gh ij ≥0.

[0148] S607 performs degamma processing on the input image, reducing the grayscale value to Grey. ij Convert to linear spatial brightness Y ij .

[0149] S608, Calculate the compensated input image brightness Y ij_comp =M ij *Y ij For the principal image Y ij_comp =Y ij .

[0150] S609, Perform regamma processing on the input image, Y ij_comp Transformation to nonlinear space Grey ij_comp , and obtain the ghost image compensation diagram.

[0151] S610, complete ghost image compensation.

[0152] Further, in some embodiments, compensating the original input image according to the first compensation matrix to obtain a ghost image compensation map of the original input image includes: converting the original input image from a nonlinear color space to a linear color space to obtain the linear space brightness and color of each position of the original input image; obtaining the compensated original input image according to the linear space brightness and color of each position of the original input image, the first compensation matrix of each position of the main image, and the first compensation matrix of each position other than the main image, wherein the first compensation matrix of each position of the main image is 1; and converting the compensated original input image from the linear color space to a nonlinear color space to obtain the ghost image compensation map.

[0153] Specifically, embodiments of this application can convert the original input image from a non-linear color space to a linear color space by performing degamma processing on the original input image, that is, the RGB color value [R] of each pixel of the input image is converted to a linear color space. ij G ij B ij Convert to linear color space [X] ij ,Y ij Z ij ].

[0154] Furthermore, the first compensation matrix M obtained from the above calculation is used... ij The brightness and color of each pixel in the linear color space are compensated to obtain the compensated brightness and color of the input image [X]. ij_comp ,Y ij_comp Z ij_comp ], where [X ij_comp ,Y ij_comp Z ij_comp ] = M ij *[X ij ,Y ij Z ij ].

[0155] Furthermore, embodiments of this application can perform regamma processing on the image to convert the compensated linear space image back to a non-linear color space, that is, to convert the brightness and color of the compensated input image [X] back to a non-linear color space. ij_comp ,Y ij_comp Z ij_comp Convert ] to RGB color values ​​in a non-linear color space [R ij_comp G ij_comp B ij_comp The resulting ghost image compensation image shows the pre-compensation effect of the ghost image as follows: Figure 7 As shown.

[0156] To facilitate understanding by those skilled in the art of completing the ghost image compensation map when the brightness and color of the ghost image are inconsistent with the brightness and color of the background in the embodiments of this application, the following is combined with... Figure 8 Please provide a detailed explanation.

[0157] like Figure 8 As shown, when the brightness and color of the ghost image are inconsistent with the brightness and color of the background, the process of completing the ghost image compensation map includes the following steps:

[0158] S801, Start.

[0159] S802, determine the original input image and eye point position.

[0160] S803, obtain the positions (i,j) and corresponding brightness and color [X_Gh] of multiple ghost images in the input image through LUT. ij ,Y_Gh ij ,Z_Gh ij ].

[0161] S804, obtain the brightness and color [X] of all locations in the background based on the input image. ij ,Y ij Z ij ].

[0162] S805, Calculate the second compensation matrix P ij And the background coefficient N, so that the position of the ghost image with the maximum brightness satisfies N*[X ij ,Y ij Z ij ] = P ij *[X ij ,Y ij Z ij ]+Max[X_Gh ij ,Y_Gh ij ,Z_Gh ij ].

[0163] S806, set Goal = N*[X ij ,Y ij Z ij [Target brightness and color]. Calculate the first compensation matrix M. ij Make the brightness and color of every position except the main image satisfy Goal = M ij *[X ij ,Y ij Z ij ]+[X_Gh ij ,Y_Gh ij ,Z_Gh ij ].

[0164] S807, performs degamma processing on the input image, [Rij G ij B ij Transform into linear space [X] ij ,y ij Z ij ].

[0165] S808, calculate the brightness and color of the compensated input image: [X ij_comp ,y ij_comp Z ij_comp ] = M ij *[X ij ,Y ij Z ij ].

[0166] S809, perform regamma processing on the image, [X ij_comp ,Y ij_comp Z ij_comp Transform to nonlinear space [R] ij_comp G ij_comp B ij_comp ], thus obtaining the ghost image compensation diagram.

[0167] S810, complete ghost image compensation.

[0168] The ghosting removal method proposed in this application determines multiple ghosting positions corresponding to the main image and the brightness and color of each ghosting position based on the current eye point position. It also determines a brightness compensation coefficient or a first compensation matrix for the original input image and compensates the original input image according to the brightness compensation coefficient or the first compensation matrix to obtain a ghosting compensation map of the original input image. This solves the problems of related technologies failing to completely eliminate all ghosting and the cumbersome process of improvement. Without changing the optical design and optical process, ghosting is eliminated by using image algorithms to change the background brightness and color, thereby improving display quality and user experience.

[0169] Next, the ghost image elimination device according to the embodiments of this application is described with reference to the accompanying drawings.

[0170] Figure 9 This is a block diagram of a ghost removal device according to an embodiment of this application.

[0171] like Figure 9 As shown, the ghost image removal device 10 includes: an acquisition module 100, a determination module 200, and an image compensation module 300.

[0172] The acquisition module 100 is used to acquire the original input image and the current eye point position, wherein the original input image includes the main image and the background; the determination module 200 is used to determine multiple ghost image positions corresponding to the main image and the brightness and color of each ghost image position based on the current eye point position, and to determine the brightness compensation coefficient or the first compensation matrix of the original input image based on the brightness and color of each ghost image position and the brightness and color of all positions of the background; the image compensation module 300 is used to compensate the original input image according to the brightness compensation coefficient or the first compensation matrix to obtain the ghost image compensation map of the original input image.

[0173] Furthermore, in some embodiments, the determining module 200 is configured to: determine the target brightness for ghost image compensation when the colors of multiple ghost images are consistent with the background color, and calculate the brightness compensation coefficient for each position except the main image based on the target brightness.

[0174] Furthermore, in some embodiments, the image compensation module 300 is used to: convert the original input image from a nonlinear color space to a linear color space to obtain the linear space brightness of each position of the original input image; obtain the compensated original input image based on the linear space brightness of each position of the original input image, the brightness compensation coefficient of each position of the main image, and the brightness compensation coefficient of each position other than the main image, wherein the brightness compensation coefficient of each position of the main image is 1; and convert the compensated original input image from the linear color space to a nonlinear color space to obtain a ghost image compensation map.

[0175] Furthermore, in some embodiments, the determining module 200 is used to: obtain the target brightness based on the sum of the brightness of the brightest ghost image among a plurality of ghost images and the background brightness at the corresponding position of the ghost image.

[0176] Furthermore, in some embodiments, after determining whether the colors of the multiple ghost images are consistent with the background color, the determining module 200 is further configured to: when the colors of the multiple ghost images are inconsistent with the background color, calculate a target background coefficient based on the brightness color of the ghost image with the highest brightness among the multiple ghost images and the brightness color of the background at the corresponding position of the ghost image; calculate the target brightness color of each position of the background based on the target background coefficient, and calculate a first compensation matrix for each position other than the main image based on the target brightness color of each position of the background.

[0177] Further, in some embodiments, the image compensation module 300 is used to: convert the original input image from a nonlinear color space to a linear color space to obtain the linear space brightness and color of each position of the original input image; obtain the compensated original input image based on the linear space brightness and color of each position of the original input image, the first compensation matrix of each position of the main image, and the first compensation matrix of each position other than the main image, wherein the first compensation matrix of each position of the main image is 1; and convert the compensated original input image from the linear color space to a nonlinear color space to obtain a ghost image compensation map.

[0178] Furthermore, in some embodiments, the determining module 200 is used to: determine a background coefficient corresponding to a plurality of second compensation matrices based on the brightness and color of the ghost image with the highest brightness among the plurality of ghost images and the brightness and color of the background at the corresponding position of the ghost image, thereby obtaining a plurality of background coefficients; and take the smallest background coefficient among the plurality of background coefficients as the target background coefficient.

[0179] Furthermore, in some embodiments, the determining module 200 is used to: based on the current eye point position, obtain multiple ghost image positions corresponding to the main image and the brightness and color of each ghost image position from a preset ghost image LUT.

[0180] Furthermore, in some embodiments, before obtaining multiple ghost image positions corresponding to the main image and the brightness and color of each ghost image position from a preset ghost image LUT based on the current eye position, the determining module 200 is further configured to: determine a three-dimensional spatial region in the display device under test corresponding to the user's eye position; determine multiple positions to be lit on the display screen of the display device under test and the order of each position to be lit; based on the order of each position to be lit, sequentially use multiple solid color images to light up the current position to be lit, and traverse the three-dimensional spatial region to obtain multiple target ghost image positions corresponding to the current position to be lit at each eye position and the brightness and color of each target ghost image position; generate a preset ghost image LUT based on the multiple target ghost image positions corresponding to the current position to be lit at each eye position and the brightness and color of each target ghost image position.

[0181] It should be noted that the foregoing explanation of the ghost image removal method embodiment also applies to the ghost image removal device of this embodiment, and will not be repeated here.

[0182] The ghost image elimination device proposed in this application determines multiple ghost image positions corresponding to the main image and the brightness and color of each ghost image position based on the current eye point position. It also determines a brightness compensation coefficient or a first compensation matrix for the original input image and compensates the original input image according to the brightness compensation coefficient or the first compensation matrix to obtain a ghost image compensation map of the original input image. This solves the problems of related technologies failing to completely eliminate all ghost images and the cumbersome process of improvement. Without changing the optical design and optical process, ghost images are eliminated by using image algorithms to change the background brightness and color, thereby improving display quality and user experience.

[0183] Figure 10 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0184] The memory 1001, the processor 1002, and the computer program stored on the memory 1001 and capable of running on the processor 1002.

[0185] When the processor 1002 executes the program, it implements the ghost image elimination method provided in the above embodiments.

[0186] Furthermore, electronic devices also include:

[0187] Communication interface 1003 is used for communication between memory 1001 and processor 1002.

[0188] The memory 1001 is used to store computer programs that can run on the processor 1002.

[0189] The memory 1001 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0190] If the memory 1001, processor 1002, and communication interface 1003 are implemented independently, then the communication interface 1003, memory 1001, and processor 1002 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0191] Optionally, in a specific implementation, if the memory 1001, processor 1002, and communication interface 1003 are integrated on a single chip, then the memory 1001, processor 1002, and communication interface 1003 can communicate with each other through an internal interface.

[0192] The processor 1002 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0193] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the ghost image removal method described above.

[0194] This application also provides a computer program product, including a computer program that is executed to implement the ghost image removal method as described in the above embodiments.

[0195] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0196] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0197] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0198] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0199] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0200] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0201] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0202] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for eliminating ghost images, characterized in that, Includes the following steps: Obtain the original input image and the current eye point position, wherein the original input image includes the main image and the background; Based on the current eye point position, determine the multiple ghost image positions corresponding to the main image and the brightness and color of each ghost image position, and determine the brightness compensation coefficient or first compensation matrix of the original input image based on the brightness and color of each ghost image position and the brightness and color of all positions of the background. The original input image is compensated according to the brightness compensation coefficient or the first compensation matrix to obtain the ghost image compensation map of the original input image.

2. The method according to claim 1, characterized in that, The step of determining the brightness compensation coefficient of the original input image based on the brightness and color of each ghost image position and the brightness and color of all positions in the background includes: When the colors of the multiple ghost images are consistent with the background color, a target brightness for ghost image compensation is determined, and a brightness compensation coefficient for each position other than the main image is calculated based on the target brightness.

3. The method according to claim 2, characterized in that, The step of compensating the original input image according to the brightness compensation coefficient to obtain the ghost image compensation map of the original input image includes: The original input image is converted from a non-linear color space to a linear color space to obtain the linear space brightness of each position in the original input image; The compensated original input image is obtained based on the linear spatial brightness of each position of the original input image, the brightness compensation coefficient of each position of the main image, and the brightness compensation coefficient of each position other than the main image, wherein the brightness compensation coefficient of each position of the main image is 1. The compensated original input image is converted from the linear color space to the nonlinear color space to obtain the ghost image compensation map.

4. The method according to claim 2, characterized in that, Determining the target brightness for ghost image compensation includes: The target brightness is obtained by summing the brightness of the brightest ghost image among the plurality of ghost images and the background brightness at the corresponding position of the ghost image.

5. The method according to claim 1, characterized in that, The step of determining the first compensation matrix of the original input image based on the brightness and color of each ghost image position and the brightness and color of all positions of the background further includes: When the colors of the multiple ghost images are inconsistent with the color of the background, the target background coefficient is calculated based on the brightness color of the ghost image with the highest brightness among the multiple ghost images and the brightness color of the background at the corresponding position of the ghost image; Calculate the target brightness color at each position of the background based on the target background coefficient, and calculate the first compensation matrix for each position other than the main image based on the target brightness color at each position of the background.

6. The method according to claim 5, characterized in that, The step of compensating the original input image according to the first compensation matrix to obtain the ghost image compensation map of the original input image includes: The original input image is converted from a non-linear color space to a linear color space to obtain the linear space brightness and color of each position in the original input image; The compensated original input image is obtained based on the linear spatial brightness and color of each position of the original input image, the first compensation matrix of each position of the main image, and the first compensation matrix of each position other than the main image, wherein the first compensation matrix of each position of the main image is 1; The compensated original input image is converted from the linear color space to the nonlinear color space to obtain the ghost image compensation map.

7. The method according to claim 5, characterized in that, The step of calculating the target background coefficient based on the brightness and color of the brightest ghost image among the plurality of ghost images and the brightness and color of the background at the corresponding position of that ghost image includes: Based on the brightness and color of the brightest ghost image among the multiple ghost images and the brightness and color of the background at the corresponding position of the ghost image, a background coefficient corresponding to multiple second compensation matrices is determined, resulting in multiple background coefficients. The second compensation matrix is ​​a matrix that performs brightness and color compensation on the position of the brightest ghost image among the multiple ghost images. The minimum background coefficient among the plurality of background coefficients is taken as the target background coefficient.

8. The method according to claim 1, characterized in that, The step of determining the positions of multiple ghost images corresponding to the main image and the brightness and color of each ghost image position based on the current eye point position includes: Based on the current eye point position, obtain multiple ghost positions corresponding to the main image and the brightness and color of each ghost position from the preset ghost image LUT.

9. The method according to claim 8, characterized in that, Before obtaining the multiple ghost positions corresponding to the main image and the brightness and color of each ghost position from the preset ghost image LUT based on the current eye point position, the method further includes: Determine the three-dimensional spatial region in the display device under test that corresponds to the user's eye position; Determine multiple positions to be lit on the display screen of the display device under test and the order of each position to be lit; Based on the order of each position to be lit, the current position to be lit is lit sequentially using multiple solid color images, and the three-dimensional space region is traversed to obtain multiple target ghost positions corresponding to the current position to be lit and the brightness and color of each target ghost position at each eye point position. The preset ghost image LUT is generated based on the multiple target ghost image positions corresponding to the current position to be lit at each eye point position and the brightness and color of each target ghost image position.

10. A ghost image elimination device, characterized in that, include: The acquisition module is used to acquire the original input image and the current eye point position, wherein the original input image includes the main image and the background; The determination module is used to determine multiple ghost image positions corresponding to the main image and the brightness and color of each ghost image position based on the current eye point position, and to determine the brightness compensation coefficient or first compensation matrix of the original input image based on the brightness and color of each ghost image position and the brightness and color of all positions of the background. The image compensation module is used to compensate the original input image according to the brightness compensation coefficient or the first compensation matrix to obtain the ghost image compensation map of the original input image.

11. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the ghost removal method as described in any one of claims 1-9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the ghost image removal method as described in any one of claims 1-9.

13. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the ghost image removal method as described in any one of claims 1-9.

Images