Image dark corner correction method, electronic device, and computer-readable storage medium

Abstract

The application discloses an image dark corner correction method, an electronic device and a computer readable storage medium. The method comprises the following steps: acquiring a current frame image, and determining a transformation relationship between the current frame image and a previous frame image; wherein, the current frame image and the previous frame image correspond to an overlapping area; mapping a reference correction coefficient model of the previous frame image to the current frame image based on the transformation relationship, to obtain a first correction coefficient model of the overlapping area in the current frame image; determining a second correction coefficient model of a non-overlapping area based on a first image parameter of the current frame image and a second image parameter of the non-overlapping area in the current frame image; and performing dark corner correction on the current frame image based on the first correction coefficient model and the second correction coefficient model. The above scheme can improve the effect of image dark corner correction.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to an image vignetting correction method, an electronic device, and a computer-readable storage medium. Background Technology

[0002] Visual navigation is one of the core technologies for autonomous mobile robots (AMRs) to achieve autonomous driving. In visual navigation systems, AMRs locate and navigate by analyzing image texture features. However, images captured by cameras generally exhibit a "vignetting" phenomenon, where the center is bright and the edges are dark. This brightness attenuation reduces the texture clarity of image edge areas, affecting the accuracy of visual navigation algorithms in extracting and matching ground features, and thus reducing the overall reliability of localization.

[0003] Currently, to address the vignetting problem, existing technologies typically employ pre-calibration, where fixed vignetting correction coefficients are fitted to a standard test image under specific lighting conditions and then fixed to the camera. However, this method has significant limitations in practical applications: once factory calibration is complete, the correction coefficients remain fixed, while actual working conditions such as lighting, ground materials, and the aging of supplementary lighting can change over time, causing the correction effect of the fixed calibration coefficients to gradually decline. Therefore, improving the effectiveness of image vignetting correction has become an urgent problem to be solved. Summary of the Invention

[0004] The main technical problem addressed by this application is to provide an image vignetting correction method, electronic device, and computer-readable storage medium that can improve the effect of image vignetting correction.

[0005] To address the aforementioned technical problems, this application provides a method for correcting vignetting in an image, comprising: acquiring a current frame image and determining a transformation relationship between the current frame image and a previous frame image; wherein the current frame image and the previous frame image have overlapping regions; based on the transformation relationship, mapping a reference correction coefficient model of the previous frame image to the current frame image to obtain a first correction coefficient model of the overlapping region in the current frame image; determining a second correction coefficient model of the non-overlapping region based on a first image parameter of the current frame image and a second image parameter of a non-overlapping region in the current frame image; and performing vignetting correction on the current frame image based on the first correction coefficient model and the second correction coefficient model.

[0006] To address the aforementioned technical problems, a second aspect of this application provides an electronic device including a memory and a processor coupled to each other, wherein the memory stores program instructions and the processor executes the program instructions to implement the method described in the first aspect.

[0007] To address the aforementioned technical problems, a third aspect of this application provides a computer-readable storage medium storing program instructions executable by a processor, the program instructions being used to implement the method described in the first aspect.

[0008] The above scheme acquires the current frame image, determines the transformation relationship between the current frame image and the previous frame image, and identifies overlapping areas between the current frame image and the previous frame image. Based on the transformation relationship, the reference correction coefficient model of the previous frame image is mapped onto the current frame image, thereby obtaining the first correction coefficient model corresponding to the overlapping area in the current frame image. Based on the first image parameters of the current frame image and the second image parameters of the remaining non-overlapping areas in the current frame image, the second correction coefficient model corresponding to the non-overlapping areas is determined. Based on the first and second correction coefficient models, online vignetting correction optimization is performed on the current frame image. The transformation relationship is calculated through the motion of the preceding and following frames in a short period of time, and the correction coefficient of the current frame image is deduced based on it. This scheme can adapt to the changes in vignetting correction coefficients caused by changes in ground material and light reflection during motion, thereby improving the effect of image vignetting correction. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments 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. Wherein: Figure 1 This is a flowchart illustrating one embodiment of the image vignetting correction method of this application; Figure 2 This is a schematic diagram of the current frame image and the previous frame image in this application; Figure 3 This is a flowchart illustrating another embodiment of the image vignetting correction method of this application; Figure 4 This is a schematic diagram of an image that has not undergone vignetting correction. Figure 5 This is a schematic diagram of an image after vignetting correction. Figure 6 This is a schematic diagram of the structure of one embodiment of the electronic device of this application; Figure 7 This is a schematic diagram of one embodiment of the computer-readable storage medium of this application. Detailed Implementation

[0010] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments, and different implementation methods can be adaptively combined. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0011] In this paper, the terms "system" and "network" are often used interchangeably. The term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship. Furthermore, "many" in this paper means two or more.

[0012] Please see Figure 1 and Figure 2 , Figure 1 This is a flowchart illustrating one embodiment of the image vignetting correction method of this application. Figure 2 This is a schematic diagram of the current frame image and the previous frame image in this application. The method includes: S101: Obtain the current frame image and determine the transformation relationship between the current frame image and the previous frame image.

[0013] Specifically, by Figure 2 It can be seen that there is an overlapping area between the current frame image and the previous frame image, so the transformation relationship between the current frame image and the previous frame image can be determined.

[0014] In one application method, the pixel positions of multiple pixels in the overlapping region of the current frame image and the pixel positions of multiple pixels in the overlapping region of the previous frame image are obtained, and the transformation relationship between the current frame image and the previous frame image is calculated using the optical flow method.

[0015] In another application, the pixel positions of multiple pixels in the overlapping region of the current frame image and the pixel positions of multiple pixels in the overlapping region of the previous frame image are obtained, and the transformation relationship between the current frame image and the previous frame image is calculated using the feature point method.

[0016] S102: Based on the transformation relationship, the reference correction coefficient model of the previous frame image is mapped to the current frame image to obtain the first correction coefficient model of the overlapping area in the current frame image.

[0017] Specifically, based on the transformation relationship, the reference correction coefficient model of the previous frame image is mapped onto the current frame image, thereby obtaining the first correction coefficient model corresponding to the overlapping area in the current frame image.

[0018] In one application method, for each pixel in the overlapping region of the current frame image, the inverse transformation of the transformation relationship is used to calculate its corresponding position in the previous frame image. Then, using the reference correction coefficient model of the previous frame image, interpolation is performed around the position of each pixel to obtain the inherited correction coefficient of that pixel. All the obtained inherited correction coefficients are used to construct the first correction coefficient model corresponding to the overlapping region in the current frame image.

[0019] In another application, the reference correction coefficient model of the previous frame image is used as an image, where the coefficient value corresponding to each pixel is used as the gray value of that pixel. Based on the coordinate space of the current frame image, the transformation relationship is directly used to perform a geometric transformation on the entire coefficient image to obtain the mapped coefficient map. All effective pixel areas in the map are the overlapping areas, and the corresponding pixel values ​​are the first correction coefficient model.

[0020] S103: Determine the second correction coefficient model for the non-overlapping region based on the first image parameters of the current frame image and the second image parameters of the non-overlapping region in the current frame image.

[0021] Specifically, based on the first image parameters of the current frame image and the second image parameters of the remaining non-overlapping regions in the current frame image, the second correction coefficient model corresponding to the non-overlapping regions is determined.

[0022] In one application method, a reference value for the image center is calculated based on the image width and image height of the current frame image, which serves as the first image parameter for the image center. This parameter is used to normalize the pixel distance, enabling the model to use images of different resolutions. For each pixel in the non-overlapping region, the Euclidean distance from that pixel to the image center is calculated as the second image parameter. The image center coordinates can be directly obtained from the image width and image height of the current frame image. The first and second image parameters are substituted into a preset vignetting circle correction coefficient model, and the non-overlapping region pixels are traversed to generate a second correction coefficient model.

[0023] In another application, the image width and height of the current frame image are used as basic parameters, and combined with the preset ellipse shape parameters, the first image parameter is determined. For each pixel in the non-overlapping region, the normalized elliptical distance from the pixel to the image center is calculated as the second image parameter. The first and second image parameters are substituted into the preset ellipse correction coefficient model, and the pixels in the non-overlapping region are traversed to generate the second correction coefficient model.

[0024] S104: Perform vignetting correction on the current frame image based on the first correction coefficient model and the second correction coefficient model.

[0025] Specifically, based on the first correction coefficient model and the second correction coefficient model, online vignetting correction optimization is performed on the current frame image.

[0026] In one application method, a coefficient map with the same resolution as the current frame image is created, with the initial value set to 0, i.e., no correction. For each pixel position belonging to the overlapping area, its coefficient value in the first correction coefficient model is filled into the corresponding position of the coefficient map. For each pixel position in the non-overlapping area, its coefficient value in the second correction coefficient model is filled into the corresponding position of the coefficient map. The current complete coefficient map is multiplied pixel by pixel with the current frame image to achieve vignetting correction.

[0027] In another application, an overlapping region sub-image is extracted from the current frame image, and each pixel of the sub-image is multiplied and corrected using a first correction coefficient model to obtain a first corrected sub-image. A non-overlapping region sub-image is extracted from the current frame image, and each pixel of the sub-image is multiplied and corrected using a second correction coefficient model to obtain a second corrected sub-image. The first and second corrected sub-images are then combined back into a complete image according to their respective geometric positions, thereby achieving vignetting correction.

[0028] The above scheme acquires the current frame image, determines the transformation relationship between the current frame image and the previous frame image, and identifies overlapping areas between the current frame image and the previous frame image. Based on the transformation relationship, the reference correction coefficient model of the previous frame image is mapped onto the current frame image, thereby obtaining the first correction coefficient model corresponding to the overlapping area in the current frame image. Based on the first image parameters of the current frame image and the second image parameters of the remaining non-overlapping areas in the current frame image, the second correction coefficient model corresponding to the non-overlapping areas is determined. Based on the first and second correction coefficient models, online vignetting correction optimization is performed on the current frame image. The transformation relationship is calculated through the motion of the preceding and following frames in a short period of time, and the correction coefficient of the current frame image is deduced based on it. This scheme can adapt to the changes in vignetting correction coefficients caused by changes in ground material and light reflection during motion, thereby improving the effect of image vignetting correction.

[0029] Please see Figure 3 , Figure 4 and Figure 5 , Figure 3 This is a flowchart illustrating another embodiment of the image vignetting correction method of this application. Figure 4 This is a schematic diagram of an image that has not undergone vignetting correction. Figure 5 This is a schematic diagram of an image after vignetting correction. The method includes: S201: Obtain the current frame image and determine the transformation relationship between the current frame image and the previous frame image.

[0030] Specifically, the current frame image is acquired, and the transformation relationship between the current frame image and the previous frame image is determined, wherein there is an overlapping area between the current frame image and the previous frame image.

[0031] In one embodiment, the current frame image is captured by a camera on an autonomous mobile robot. The camera is equipped with a ring light, and the camera and the ring light are positioned below the autonomous mobile robot, with the ring light being circular in shape.

[0032] Specifically, because the camera used in this invention is mounted below the autonomous mobile robot, and the ring light is circular, the vignetting effect formed on the imaging plane also conforms to a concentric circle attenuation distribution, such as... Figure 4 As shown, the central part is brighter and the surrounding parts are darker, exhibiting a pattern of brightness attenuation from the center of the image outwards. This pattern can serve as the basic assumption and fixed model for the image vignetting correction method of this application. The specific processing flow can be found in the following description.

[0033] In other embodiments, the camera used in this invention may also be mounted under other movable handling robots or handling trolleys, and this application does not impose specific limitations on this.

[0034] In one implementation scenario, determining the transformation relationship between the current frame image and the previous frame image in step S201 specifically includes: obtaining multiple first feature points in the overlapping area of ​​the current frame image and multiple second feature points in the overlapping area of ​​the previous frame image; and obtaining the transformation relationship based on the position coordinates of the multiple first feature points and the position coordinates of the multiple second feature points.

[0035] Specifically, using a feature point extraction algorithm, significant corner points or spots in the overlapping areas of the current frame image and the previous frame image are detected as the first feature point and the second feature point, respectively. The two sets of feature points are paired, and based on the position coordinates of the matched point pairs, an equation is constructed and the homography transformation matrix is ​​robustly estimated using the Random Sample Consensus (RANSAC) algorithm. This homography transformation matrix is ​​then used as the transformation relation.

[0036] In a specific implementation scenario, the transformation relationship between the current frame image and the previous frame image is calculated using the following formula: (1) in, This indicates the position coordinates of the first feature point within the overlapping region of the current frame image. H represents the position coordinates of the second feature point within the overlapping area of ​​the previous frame image, and H represents the homography transformation matrix, i.e., the transformation relationship.

[0037] S202: Obtain the first grayscale value of the first pixel in the overlapping region from the current frame image.

[0038] Specifically, each pixel in the image corresponds to a grayscale value, and the first grayscale value of each first pixel in the overlapping region is determined from the current frame image.

[0039] S203: Based on the transformation relationship, determine the second gray value of the second pixel in the overlapping region from the previous frame image.

[0040] Specifically, based on the transformation relationship, a second pixel point that matches each first pixel point in the overlapping region is determined from the previous frame image, and a second grayscale value of each second pixel point is determined.

[0041] It should be noted that the second grayscale value of the second pixel in the previous frame image has been corrected.

[0042] S204: Based on the first gray value, the second gray value, and the reference correction coefficient model, the first correction coefficient model is obtained.

[0043] Specifically, based on the first gray value, the second gray value, and the reference correction coefficient model, the first correction coefficient model of the overlapping area in the current frame image is obtained. The historical correction coefficient is dynamically scaled by the gray value ratio of the matching pixels between frames, so as to achieve adaptive adjustment of the vignetting correction intensity to the current illumination with extremely low computing power, while ensuring the temporal continuity and spatial accuracy of the correction effect.

[0044] In one implementation scenario, step S204 specifically includes: dividing the first gray value by the second gray value as the first value; multiplying the first value by the reference correction coefficient model to obtain the first correction coefficient model.

[0045] Specifically, the value obtained by dividing the first gray value and the second gray value is used as the first value. The first value is then multiplied by the reference correction coefficient model to obtain the first correction coefficient model of the overlapping area in the current frame image. The historical correction model is scaled up as a whole using the gray ratio of the matching pixels. This achieves real-time synchronization between the vignetting correction intensity and the current illumination with extremely low computing power, avoiding overcorrection or undercorrection caused by brightness fluctuations.

[0046] In a specific implementation scenario, the first correction coefficient model is calculated using the following formula: (2) in, This represents the first correction coefficient model. This represents the reference correction coefficient model. This represents the first grayscale value. This represents the second grayscale value.

[0047] S205: Determine the second correction coefficient model for the non-overlapping region based on the first image parameters of the current frame image and the second image parameters of the non-overlapping region in the current frame image.

[0048] Specifically, based on the first image parameters of the current frame image and the second image parameters of the remaining non-overlapping regions in the current frame image, the second correction coefficient model corresponding to the non-overlapping regions is determined.

[0049] In one implementation scenario, step S205 specifically includes: determining the center coordinates of the image center of the current frame image based on the image width and image height; obtaining the pixel distance between each third pixel and the image center based on the pixel coordinates and center coordinates of each third pixel in the non-overlapping region, and using the pixel distance as a second image parameter; using the sum of the squares of the image width and image height as a first image parameter, and inputting the first image parameter and the second image parameter into a pre-established vignetting circle correction coefficient model to obtain a second correction coefficient model for the non-overlapping region.

[0050] Specifically, the center coordinates are determined based on the image width and image height of the current frame image. The pixel distance between each third pixel point in the non-overlapping region and the center is calculated as the second image parameter. The sum of the squares of the image width and height is used as the first image parameter. Both are input into the vignetting circle correction coefficient model to obtain the second correction coefficient model. This provides a correction coefficient that conforms to the preset physical model for the non-overlapping region, ensuring smooth splicing with the first correction coefficient model.

[0051] In a specific implementation scenario, the second correction coefficient model for the non-overlapping region is calculated using the following formula: (3) (4) (5) Where α represents the vignetting circle correction coefficient model, dis represents the distance between the pixel and the image center, which is the origin of the coordinate system, radius represents the sum of the squares of the image width and the image height, col represents the image width, and row represents the image height.

[0052] S206: Perform vignetting correction on the current frame image based on the first correction coefficient model and the second correction coefficient model.

[0053] Specifically, based on the first correction coefficient model and the second correction coefficient model, online vignetting correction optimization is performed on the current frame image.

[0054] In one implementation scenario, step S206 specifically includes: multiplying the first correction coefficient model with the pixels of the overlapping region to obtain the first corrected image; multiplying the second correction coefficient model with the pixels of the non-overlapping region to obtain the second corrected image; and performing vignetting correction on the current frame image based on the first corrected image and the second corrected image.

[0055] Specifically, the coefficient values ​​of each pixel in the first correction coefficient model are multiplied point-by-point by the original grayscale values ​​of the corresponding pixels in the overlapping area of ​​the current frame image to compensate for the vignetting brightness of that area, generating the first corrected image. Similarly, the second correction coefficient model is multiplied point-by-point by the pixels in the non-overlapping area to generate the second corrected image. The two corrected areas are then directly combined according to their respective geometric positions in the original image to obtain the complete vignetting correction image, as shown below. Figure 5 As shown, after the image vignetting correction method of this application is used to correct the vignetting of the image, the image as a whole becomes brighter, and the image vignetting correction effect is good.

[0056] Optionally, if there are slight differences at the boundary between the two regions, weighted fusion can be performed in the boundary transition zone to make the correction effect smoother and more natural. This method of independent correction and reintegration in different regions ensures both the high accuracy of the historical optimization coefficients inherited in overlapping regions and the fast response of the model calculation coefficients in non-overlapping regions.

[0057] In a specific implementation scenario, the corrected image is calculated using the following formula: (6) in, This refers to the pixels in the corrected image. This represents the correction coefficient model. This represents the pixels in the image before correction.

[0058] It should be noted that before the autonomous mobile robot starts running, an initial image frame is acquired and the initial image frame is corrected using formulas (3)-(6) to obtain the corrected initial image. When the current frame image is the second frame relative to the initial image frame and the robot has started running, the initial corrected image is used as the basis for vignetting correction of the current frame image in order to ensure the tracking effect of the subsequent dense optical flow.

[0059] S207: Combine the first correction coefficient model and the second correction coefficient model to obtain the current correction coefficient model corresponding to the current frame image, and use the current correction coefficient model as the reference correction coefficient model for the next frame image.

[0060] Specifically, the coefficients of the overlapping region covered by the first correction coefficient model and the coefficients of the non-overlapping region covered by the second correction coefficient model are concatenated according to their respective pixel positions in the original image to form a complete current correction coefficient model with the same resolution as the current frame image. This model fully describes the vignetting correction amount for each pixel in the current frame. When processing the next frame image, this current correction coefficient model is used as the reference correction coefficient model for the next frame, thereby passing the optimized vignetting compensation information of the current frame to the next frame, realizing iterative updates and online optimization of the correction coefficients along the time sequence.

[0061] Please see Figure 6 , Figure 6 This is a schematic diagram of an embodiment of the electronic device 60 of this application. The electronic device 60 includes a memory 600 and a processor 602 coupled to each other. The memory 600 stores program instructions (not shown). The processor 602 calls the program instructions to implement the methods in any of the above embodiments. For related descriptions, please refer to the detailed description of the above method embodiments, which will not be repeated here. Specifically, the electronic device 60 includes: desktop computers, laptops, tablet computers, servers, etc., which are not limited here. In addition, the processor 602 can also be called a central processing unit (CPU). The processor 602 may be an integrated circuit chip with signal processing capabilities. The processor 602 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor. In addition, the processor 602 can be implemented by integrated circuit chips.

[0062] Please see Figure 7 , Figure 7 This is a schematic diagram of a computer-readable storage medium according to an embodiment of the present application. The computer-readable storage medium 70 stores program instructions 700. When the program instructions 700 are executed by the processor, they implement the methods in any of the above embodiments. For a detailed description of the relevant content, please refer to the detailed description of the above method embodiments, which will not be repeated here.

[0063] It should be noted that 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0064] 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.

[0065] 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.) or processor to execute all or part of the steps of the methods of 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.

[0066] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for correcting vignetting in images, characterized in that, include: The current frame image is acquired, and the transformation relationship between the current frame image and the previous frame image is determined; wherein, there is an overlapping area between the current frame image and the previous frame image; Based on the transformation relationship, the reference correction coefficient model of the previous frame image is mapped to the current frame image to obtain the first correction coefficient model of the overlapping region in the current frame image; Based on the first image parameters of the current frame image and the second image parameters of the non-overlapping region in the current frame image, determine the second correction coefficient model of the non-overlapping region; The current frame image is vignetting corrected based on the first correction coefficient model and the second correction coefficient model.

2. The method according to claim 1, characterized in that, Determining the transformation relationship between the current frame image and the previous frame image includes: Obtain multiple first feature points of the overlapping region in the current frame image and multiple second feature points of the overlapping region in the previous frame image; The transformation relationship is obtained based on the position coordinates of multiple first feature points and multiple second feature points.

3. The method according to claim 1, characterized in that, The step of mapping the reference correction coefficient model of the previous frame image to the current frame image based on the transformation relationship to obtain the first correction coefficient model of the overlapping region in the current frame image includes: Obtain the first grayscale value of the first pixel in the overlapping region from the current frame image; Based on the transformation relationship, the second grayscale value of the second pixel in the overlapping region is determined from the previous frame image; The first correction coefficient model is obtained based on the first gray value, the second gray value, and the reference correction coefficient model.

4. The method according to claim 3, characterized in that, The process of obtaining the first correction coefficient model based on the first gray value, the second gray value, and the reference correction coefficient model includes: The value obtained by dividing the first gray value by the second gray value is taken as the first numerical value; Multiply the first value by the reference correction coefficient model to obtain the first correction coefficient model.

5. The method according to claim 1, characterized in that, The current frame image is captured by a camera on an autonomous mobile robot. The camera is equipped with a ring light, and the camera and the ring light are positioned below the autonomous mobile robot. The ring light is circular in shape.

6. The method according to claim 5, characterized in that, The step of determining the second correction coefficient model for the non-overlapping region based on the first image parameters of the current frame image and the second image parameters of the non-overlapping region in the current frame image includes: Based on the image width and image height of the current frame image, determine the center coordinates of the image center of the current frame image; Based on the pixel coordinates of each third pixel in the non-overlapping region and the center coordinates, the pixel distance between each third pixel and the image center is obtained, and the pixel distance is used as the second image parameter; The sum of the squares of the image width and the image height is used as the first image parameter. The first image parameter and the second image parameter are then input into a pre-established vignetting circle correction coefficient model to obtain the second correction coefficient model for the non-overlapping region.

7. The method according to claim 1, characterized in that, The step of correcting vignetting in the current frame image based on the first correction coefficient model and the second correction coefficient model includes: Multiply the first correction coefficient model by the pixels of the overlapping region to obtain the first corrected image; Multiply the second correction coefficient model by the pixels in the non-overlapping region to obtain the second corrected image; Based on the first corrected image and the second corrected image, the current frame image is vignetting corrected.

8. The method according to claim 1, characterized in that, After performing vignetting correction on the current frame image based on the first correction coefficient model and the second correction coefficient model, the method further includes: The first correction coefficient model and the second correction coefficient model are combined to obtain the current correction coefficient model corresponding to the current frame image, and the current correction coefficient model is used as the reference correction coefficient model for the next frame image.

9. An electronic device, characterized in that, The method includes a memory and a processor coupled to each other, the memory storing program instructions, and the processor executing the program instructions to implement the method according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The system stores program instructions that can be executed by a processor, the program instructions being used to implement the method according to any one of claims 1-8.

Images