A method for eliminating grid-shaped shadows in spliced images based on the consistency of overlapping regions

Abstract

The present invention discloses a method for eliminating grid-shaped shadows in a spliced image based on the consistency of overlapping regions, and the specific steps are as follows: Obtain an image sequence with partially overlapping adjacent regions; Use a calibration image taken under a uniform light source, select the central region as a reference for the ideal gray value, and calculate the deviation image; Based on the deviation image, establish a basic vignetting correction model through spherical fitting; Determine the overlapping region through an image registration algorithm, and calculate the sum of gray value errors within the overlapping region; By minimizing the total sum of gray value errors in all overlapping regions, determine the optimal correction coefficient and dynamically adjust the correction model; Finally, use the updated correction model to correct the image sequence to eliminate grid-shaped shadows. This method improves the brightness uniformity and visual consistency of the spliced image, and can effectively eliminate grid-shaped shadows caused by the vignetting effect.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and particularly to a method for eliminating grid-shaped shadows in a stitched image based on the consistency of overlapping regions. Background Art

[0002] In optical imaging instruments, due to the limitation of the camera's field of view, when observing a large sample, the entire sample cannot be captured in a single field of view. In this case, serial image stitching is usually required, that is, the imaging unit cooperates with a moving platform to capture images of different positions of the sample and stitch them into a panoramic image.

[0003] In an optical imaging system, the farther the light rays are from the optical axis, the smaller the effective aperture stop of the optical system they pass through, and the lower the light intensity on the imaging plane. This results in a vignetting phenomenon where the image is bright in the middle and dark at the edges. If the vignetting phenomenon cannot be effectively suppressed, a distinct grid effect will occur during the stitching process of serial images. Therefore, it is necessary to perform vignetting correction on a single image before image stitching.

[0004] Vignetting image correction is usually based on two methods: a hardware-based calibration method and an image-based feature method. The hardware-based calibration method calculates a gray-scale attenuation model by photographing a uniform calibration plate in a scene with uniform illumination. The model obtained by this method has high accuracy and is widely used in industrial fields with stable scenes. However, this method is only applicable to the optical environment during calibration, and when the environment changes, recalibration is necessary. The image feature-based method corrects vignetting using the features of a single image or multiple images, and minimizes the logarithmic entropy for vignetting correction. However, a single logarithmic entropy is not applicable to all scenes, especially in serial stitched images, where it is difficult to maintain the consistency of the processing results of multiple frames. Summary of the Invention

[0005] Technical Objective: Aiming at the deficiencies of existing vignetting correction technologies, the present invention discloses a method for eliminating grid-shaped shadows in a stitched image based on the consistency of overlapping regions, which can effectively suppress the grid shadow phenomenon in serial stitched images.

[0006] Technical Solution: To achieve the above technical objective, the present invention adopts the following technical solution:

[0007] A method for eliminating grid-shaped shadows in a stitched image based on the consistency of overlapping regions specifically includes the following steps:

[0008] Obtain an image sequence to be stitched, where adjacent images in the image sequence have partial overlapping regions;

[0009] Obtain the calibration images taken under a uniform light source, select the central region of the calibration images as the reference region for the ideal gray value, calculate the deviation of the gray value of each pixel point in the calibration images relative to the reference region, and generate a deviation image;

[0010] Based on the generated deviation image, establish a basic vignetting correction model through spherical fitting;

[0011] Determine the overlapping regions between each adjacent image through an image registration algorithm, and calculate the sum of the gray value errors of the corresponding pixel points in each overlapping region;

[0012] Based on the sum of the gray value errors of the corresponding pixel points in each overlapping region, construct the total sum of the gray value errors of the corresponding pixel points in all overlapping regions;

[0013] By minimizing the sum of the gray value errors of the corresponding pixel points in all overlapping regions, determine the optimal correction coefficient, and dynamically adjust the basic vignetting correction model according to the optimal correction coefficient to obtain an updated correction model;

[0014] Use the updated correction model to correct the image sequence to be stitched, generate a corrected image sequence, complete vignetting correction, and eliminate the grid-like shadow.

[0015] Preferably, the form of the deviation image is:

[0016] I dif =I i -I c

[0017] where, I dif represents the deviation image, I i represents the ideal gray value, that is, the average value of all gray values equal to I c in the central region, and I c is the gray value of the calibration image.

[0018] Preferably, based on the generated deviation image, the specific steps of establishing a basic vignetting correction model through spherical fitting are as follows:

[0019] Regard each pixel point in the deviation image as a point in three-dimensional space, based on the characteristics of the deviation image, establish a spherical fitting model, and the spherical equation used is:

[0020] (x - a) 2 +(y - b) 2 +(z - c) 2 =R 2

[0021] where, x and y represent the two-dimensional coordinates of the pixel point, z represents the gray value of the pixel point, a, b, and c represent the coordinates of the center of the sphere, and R represents the radius of the sphere;

[0022] The constructed error function represents the fitting error, and the spherical parameters of the best fit are solved by minimizing the error function. The formula of the error function is as follows:

[0023] E = ∑(x 2 + y 2 + z 2 - Ax - By - Cz + D) 2

[0024] Among them, parameter A = 2a, parameter B = 2b, parameter C = 2c, parameter D = a 2 + b 2 + c 2 - R 2 ;

[0025] The calculation formulas for parameters A, B, C, and D are:

[0026]

[0027] Among them, i represents the index of the pixel points participating in the fitting, i = 0, 1, 2…, n, and n represents the total number of pixel points participating in the fitting;

[0028] According to the solved parameters, the center coordinates of the spherical center of the spherical equation and the spherical radius are determined to obtain the basic vignetting correction model. The formula of the basic vignetting correction model is as follows:

[0029]

[0030] Among them, M b represents the basic vignetting correction model, and F(x, y) represents the deformation of the spherical equation.

[0031] Preferably, the calculation formula for the sum of the gray value errors of the corresponding pixel points in each overlapping region is as follows:

[0032]

[0033] Among them, Dif(R) represents the sum of the gray value errors of the corresponding pixel points in the overlapping region, R represents the overlapping region, A jl and B jl respectively represent the gray values of the two images in the overlapping region at the pixel point coordinates (j, l), and w and h respectively represent the width and height of the overlapping region;

[0034] The calculation formula for the total sum of the gray value errors of the corresponding pixel points in all overlapping regions is as follows:

[0035]

[0036] Among them, F represents the total sum of the gray value errors of the corresponding pixel points in all overlapping regions, k represents the overlapping region index, and m represents the total number of overlapping regions.

[0037] Preferably, by minimizing the sum of the gray value errors of the corresponding pixel points in all overlapping regions, the optimal correction coefficient is determined, and the basic vignetting correction model is dynamically adjusted according to the optimal correction coefficient. The specific steps to obtain the updated correction model are as follows:

[0038] Set the value range of the correction coefficient and divide the value range into several equal parts;

[0039] Respectively combine several groups of correction coefficients within the value range with the basic vignetting correction model to perform vignetting correction on the image sequence to be stitched, and calculate the sum of the total gray value errors of the corresponding pixel points in all overlapping regions of the image sequence after vignetting correction;

[0040] Select the correction coefficient with the smallest sum of the total gray value errors of the corresponding pixel points in all overlapping regions as the optimal correction coefficient, and update the basic vignetting correction model to obtain the updated correction model.

[0041] Preferably, the calculation formula for performing vignetting correction on an image by combining several groups of correction coefficients within the value range with the basic vignetting correction model is as follows:

[0042] I ds = I + δ s M b

[0043] Where, I ds represents the image sequence after vignetting correction using the s-th group of correction coefficients, I represents the image sequence before vignetting correction, and δ s represents the s-th group of correction coefficients, and s represents the correction coefficient index.

[0044] Preferably, the calculation formula for correcting the image sequence to be stitched using the updated correction model is as follows:

[0045] I d = I + M

[0046] Where, I d represents the image sequence after correction, I represents the image sequence before correction, and M represents the updated correction model and M = δM b , δ represents the optimal correction coefficient, and M b represents the basic vignetting correction model.

[0047] Beneficial effects: A method for eliminating the grid-shaped shadow of a stitched image based on the consistency of the overlapping region provided by the present invention has the following beneficial effects:

[0048] 1. The vignetting correction model established by the present invention based on the calibration image and the spherical fitting method can accurately correct the vignetting effect in the image, eliminate the grid shadow, and make the brightness distribution of the image more uniform. By dynamically calculating the optimal correction coefficient and adjusting the correction model according to the gray value error in the overlapping area, the correction process can dynamically adapt to the changes in the vignetting effect of different images, improve the adaptability of the correction model, and eliminate the need for manual adjustment for each group of images, greatly improving the calibration accuracy and automation level, and reducing the error caused by the vignetting effect in the image.

[0049] 2. By minimizing the gray value error in the overlapping area, the present invention can ensure that the gray values in the overlapping area of the serialized images are consistent during the splicing process, avoid problems such as inconsistent brightness and grid-like shadows, thereby eliminating inconsistent brightness and splicing traces, and ensuring the smooth transition and uniformity of the images. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following briefly introduces the accompanying drawings required for the description of the embodiments or the prior art.

[0051] Figure 1 It is the overall flowchart of the method of the present invention;

[0052] Figure 2 It is the schematic diagram of the deviation image of the present invention;

[0053] Figure 3 It is the schematic diagram of the basic vignetting correction model of the present invention;

[0054] Figure 4 It is the schematic diagram of the overlapping area between sequential images in the present invention;

[0055] Figure 5 It is the image data acquisition path diagram in the embodiment of the present invention;

[0056] Figure 6 It is the comparison diagram before and after vignetting calibration of the image in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] The following more clearly and completely describes the present invention by way of a preferred embodiment in conjunction with the accompanying drawings, but the present invention is not limited to the scope of the described embodiments.

[0058] As Figure 1 shown, the present invention provides a method for eliminating grid-like shadows in spliced images based on the consistency of the overlapping area, which specifically includes the following steps:

[0059] S101. Obtain an image sequence to be spliced, where there is a partial overlapping area between two adjacent images in the image sequence.

[0060] S102. Obtain a calibration image taken under a uniform light source, select the central region of the calibration image as the reference region for the ideal gray value, calculate the deviation of the gray value of each pixel point in the calibration image relative to the reference region, and generate a deviation image.

[0061] Ideally, when the camera images a uniform target, the gray values of all pixel points in the obtained image should theoretically be the same. However, due to the existence of vignetting, the gray values of each pixel point will gradually decrease as the distance from the imaging center increases. In one embodiment, the gray value of the central region of the calibration image is selected as the ideal gray value, and the difference is taken between it and the gray values of all pixel points in the image to obtain a deviation image. The form of the deviation image is:

[0062] I dif =I i -I c

[0063] where, I dif represents the deviation image, I i represents the ideal gray value, that is, all gray values are equal to the mean value of the central region I c , and I c is the gray value of the calibration image.

[0064] As Figure 2 shown, with the image width as the X-axis, the image height as the Y-axis, and the gray deviation value as the Z-axis, a spatial coordinate system is constructed to visualize the topography of the deviation image.

[0065] S103. Based on the generated deviation image, establish a basic vignetting correction model through spherical fitting.

[0066] Affected by external conditions such as the camera response curve, light source uniformity, and calibration plate uniformity, there will be abnormal points in the deviation image. Based on the gray distribution characteristics of the vignetting image, the present invention uses a spherical fitting vignetting model to filter out abnormal points. In one embodiment, the specific steps are as follows:

[0067] Regard each pixel point in the deviation image as a point in three-dimensional space, establish a spherical fitting model based on the characteristics of the deviation image, and the spherical equation used is:

[0068] (x - a) 2 +(y - b) 2 +(z - c) 2 =R 2

[0069] where, x and y represent the two-dimensional coordinates of the pixel point, z represents the gray value of the pixel point, a, b, and c represent the coordinates of the center of the sphere, and R represents the radius of the sphere;

[0070] Expanding the above spherical equation gives:

[0071] x 2 + y 2 + z 2 - 2ax - 2by - 2cz + a 2 + b 2 + c 2 =R 2

[0072] Construct an error function to represent the fitting error, and solve for the best-fit spherical parameters by minimizing the error function. The formula for the error function is as follows:

[0073] E = Σ(x 2 + y 2 + z 2 - Ax - By - Cz + D) 2

[0074] where the parameter A = 2a, the parameter B = 2b, the parameter C = 2c, and the parameter D = a 2 + b 2 + c 2 - R 2 ;

[0075] When the error function E is minimized, the circle formed by the parameters A, B, C, and D is closest to the fitting data. Construct the following matrix to solve for the parameters A, B, C, and D:

[0076]

[0077] Let the matrix T be:

[0078]

[0079] Multiply both sides of the solution matrix for the parameters A, B, C, and D by the matrix T on the left to get:

[0080]

[0081] After simplification, the calculation formulas for the parameters A, B, C, and D are:

[0082]

[0083] where i represents the index of the pixel points participating in the fitting, i = 0, 1, 2…, n, and n represents the total number of pixel points participating in the fitting;

[0084] Based on the parameters A, B, C, and D, the center coordinates a, b, c of the sphere and the sphere radius R can be calculated. The formulas are as follows:

[0085]

[0086] Determine the center coordinates and spherical radius of the spherical equation according to the solved parameters to obtain the basic correction model. The formula of the basic vignetting correction model is as follows:

[0087]

[0088] Among them, M b represents the basic vignetting correction model, and F(x, y) represents the deformation of the spherical equation.

[0089] As Figure 3 shown, taking the columns of the deviation image as x and the rows as y, substituting them into the above formula of the basic vignetting correction model to generate an image, and obtaining the basic vignetting correction model M b .

[0090] S104. Determine the overlapping area between each adjacent image through the image registration algorithm, and calculate the sum of the gray value errors of the corresponding pixel points in each overlapping area.

[0091] During the serialization image stitching process, through the image registration algorithm, the positional relationship between each image to be stitched and the adjacent image to be stitched can be solved. Thus, the overlapping area between each image to be stitched and the adjacent image can be obtained. As Figure 4 shown, it is a schematic diagram of the overlapping area between adjacent images to be stitched. In the figure, the rectangular wireframes A, B, and C are three images to be stitched respectively, and the areas R1 and R2 are the overlapping areas between image A and B and between image B and C respectively.

[0092] In an ideal state, due to the same shooting object, the difference in the gray values of the corresponding pixels of images A and B within the range of area R1 is 0. However, affected by the vignetting phenomenon, the difference in the corresponding pixels of the two images within the overlapping area is relatively large. In one embodiment, in the left half of area R1, the gray value of image A is greater than that of image B, and in the right half of area R1, the gray value of image B is greater than that of image A. The same applies to area R2. Therefore, a target function is constructed to minimize the sum of the differences of the corresponding pixels of all images in all overlapping areas of the panoramic image after vignetting elimination.

[0093] In one embodiment, the calculation formula for the sum of the gray value errors of the corresponding pixel points in each overlapping area is as follows:

[0094]

[0095] Among them, Dif(R) represents the sum of the gray value errors of the corresponding pixel points in the overlapping area, R represents the overlapping area, A jl and B jl respectively represent the gray values of the two images in the overlapping area at the pixel point coordinates (j, l), and w and h respectively represent the width and height of the overlapping area;

[0096] S105. Construct the total gray value error sum of corresponding pixel points in all overlapping regions based on the gray value errors of corresponding pixel points in each overlapping region.

[0097] The calculation formula for the total gray value error sum of corresponding pixel points in all overlapping regions is as follows:

[0098]

[0099] Among them, F represents the total gray value error sum of corresponding pixel points in all overlapping regions, k represents the overlapping region index, and m represents the total number of overlapping regions.

[0100] S106. Determine the optimal correction coefficient by minimizing the total gray value error sum of corresponding pixel points in all overlapping regions, and dynamically adjust the basic vignetting correction model according to the optimal correction coefficient to obtain an updated correction model.

[0101] In one embodiment, the specific steps of determining the optimal correction coefficient by minimizing the total gray value error sum of corresponding pixel points in all overlapping regions and dynamically adjusting the basic vignetting correction model according to the optimal correction coefficient to obtain an updated correction model are as follows:

[0102] S601. Set the value range of the correction coefficient and divide the value range into several equal parts.

[0103] Set the range of the correction coefficient to m~n, divide the range of m and n into s equal parts. According to experiments, it is more ideal to set the range of the correction coefficient to 0~2 and s to 20.

[0104] S602. Respectively combine several groups of correction coefficients within the value range with the basic vignetting correction model to perform vignetting correction on the image sequence to be spliced, and calculate the total gray value error sum of corresponding pixel points in all overlapping regions of the vignetting-corrected image sequence.

[0105] The calculation formula for performing vignetting correction on the image by combining several groups of correction coefficients within the value range with the basic vignetting correction model is as follows:

[0106] I ds = I + δ s M b

[0107] Among them, I ds represents the image sequence after vignetting correction using the s-th group of correction coefficients, I represents the image sequence before vignetting correction, and δ s represents the s-th group of correction coefficients, and s represents the correction coefficient index.

[0108] Bring the s groups of data in the range of m to n into the above formula for vignetting correction respectively, and calculate the error F using the calculation formula of the total gray value error sum of the corresponding pixel points in all overlapping regions for the image after vignetting correction. Select the correction coefficient value with the smallest error F as the optimal correction coefficient.

[0109] S603. Select the correction coefficient with the smallest total gray value error sum of the corresponding pixel points in all overlapping regions as the optimal correction coefficient, update the basic vignetting correction model, and obtain the updated correction model.

[0110] S107. Use the updated correction model to correct the image sequence to be stitched, generate the corrected image sequence, complete the vignetting correction, and eliminate the grid-like shadow.

[0111] The calculation formula for using the updated correction model to correct the image sequence to be stitched is as follows:

[0112] I d = I + M

[0113] where, I d represents the corrected image sequence, I represents the image sequence before correction, M represents the updated correction model and M = δM b , δ represents the optimal correction coefficient, and M b represents the basic vignetting correction model.

[0114] In a practical application of the present invention, a super-depth-of-field digital microscope is used to collect a serialized image data set, and this data set is a vignetting image generated by optical characteristics. The image acquisition method is: in the X direction, taking 2 / 3 of the image width as the step distance, and in the Y direction, taking 2 / 3 of the image height as the step distance, and collecting the vignetting image sequence along an S-shaped trajectory. As Figure 5 shown in the image acquisition path diagram, the camera moves along the path from 1 to 9 and collects images. By performing local matching and global optimization on the image sequence, determine the position of each image to be stitched on the panoramic image. Calculate the overlapping region R according to the position of each image to be stitched on the panoramic image, solve the correction coefficient, perform vignetting correction using the correction model, and fuse the images.

[0115] When using the vignetting image generated by optical characteristics as the data set to apply the method provided by the present invention for vignetting calibration to eliminate the grid-like shadow, the comparison diagrams before and after calibration are as Figure 6 shown. The left side of the figure is the image before vignetting calibration, and the right side is the image after vignetting calibration. After correcting all the images to be stitched using the method of the present invention and then stitching and fusing them, the obtained panoramic image has better uniformity and weakens the grid-like shadow in the panoramic image.

[0116] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for removing grid-like shadows from spliced ​​images based on consistency of overlapping areas, characterized in that: The specific steps include: Acquire an image sequence to be stitched, where two adjacent images in the image sequence have a partial overlapping area; Obtain a calibration image taken under a uniform light source, select the central area of ​​the calibration image as the reference area of ​​the ideal grayscale value, calculate the deviation of the grayscale value of each pixel in the calibration image relative to the reference area, and generate a deviation image; Based on the generated deviation image, a basic vignetting correction model is established through spherical fitting; the specific steps include: Each pixel point in the deviation image is regarded as a point in three-dimensional space, and a spherical fitting model is established based on the characteristics of the deviation image; an error function is constructed to represent the fitting error, and the best fitting spherical parameters are solved by minimizing the error function; the spherical radius of the spherical center coordinates of the spherical equation is determined according to the solved parameters, and a basic vignetting correction model is obtained; Determine the overlapping area between each adjacent image through the image registration algorithm, and calculate the gray value error sum of the corresponding pixel points in each overlapping area; Based on the gray value error sum of the corresponding pixel points in each overlapping area, a total gray value error sum of the corresponding pixel points in all overlapping areas is constructed; The optimal correction coefficient is determined by minimizing the sum of grayscale value errors of corresponding pixels in all overlapping areas, and the basic vignetting correction model is dynamically adjusted according to the optimal correction coefficient to obtain an updated correction model; wherein the optimal correction coefficient is determined by minimizing the sum of grayscale value errors of corresponding pixels in all overlapping areas, and the specific steps include: The value range of the correction coefficient is set, and the value range is evenly divided into several parts; several groups of correction coefficients within the value range are combined with the basic vignetting correction model to perform vignetting correction on the image sequence to be spliced, and the total gray value error sum of the corresponding pixels in all overlapping areas of the image sequence after vignetting correction is calculated; the correction coefficient with the smallest total gray value error sum of the corresponding pixels in all overlapping areas is selected as the optimal correction coefficient; The updated correction model is used to correct the image sequence to be stitched, generate a corrected image sequence, complete vignetting correction, and eliminate grid-like shadows.

2. The method for removing grid-like shadows from spliced ​​images based on consistency of overlapping areas according to claim 1, characterized in that: The deviation image is of the form: I dif =I i -I c Among them, I dif represents the deviation image, I i Represents the ideal grayscale value, that is, all grayscale values ​​are equal to I c The mean value of the central area, I c is the gray value of the calibration image.

3. The method for removing grid-like shadows from spliced ​​images based on consistency of overlapping areas according to claim 1, characterized in that: Based on the generated deviation image, the basic vignetting correction model is established through spherical fitting. The specific steps are as follows: Each pixel in the deviation image is regarded as a point in three-dimensional space. Based on the characteristics of the deviation image, a spherical fitting model is established. The spherical equation used is: (x-a) 2 +(y-b) 2 +(z-c) 2 =R 2 Among them, x and y represent the two-dimensional coordinates of the pixel point, z represents the gray value of the pixel point, a, b and c represent the coordinates of the sphere center, and R represents the radius of the sphere; An error function is constructed to represent the fitting error, and the best fitting spherical parameters are solved by minimizing the error function. The formula of the error function is as follows: E=∑(x 2 +y 2 +z 2 -Ax-By-Cz+D) 2 Among them, parameter A = 2a, parameter B = 2b, parameter C = 2c, parameter D = a 2 +b 2 +c 2 -R 2 ; The calculation formulas for parameters A, B, C and D are: Wherein, i represents the index of the pixel point involved in fitting, i=0, 1, 2…, n, and n represents the total number of pixel points involved in fitting; The spherical radius of the spherical center coordinates of the spherical equation is determined according to the solved parameters, and the basic vignetting correction model is obtained. The formula of the basic vignetting correction model is as follows: Among them, M b represents the basic vignetting correction model, and F(x, y) represents the spherical equation deformation.

4. The method for removing grid-like shadows from spliced ​​images based on consistency of overlapping areas according to claim 1, characterized in that: The calculation formula for the sum of the gray value errors of corresponding pixels in each overlapping area is as follows: Among them, Dif(R) represents the gray value error sum of corresponding pixels in the overlapping area, R represents the overlapping area, and A jl and B jl Respectively represent the grayscale values ​​of the two images in the overlapping area at the pixel coordinates (j, l), w and h represent the width and height of the overlapping area respectively; The calculation formula for the total gray value error of corresponding pixels in all overlapping areas is as follows: Among them, F represents the total gray value error sum of corresponding pixels in all overlapping areas, k represents the overlapping area index, and m represents the total number of overlapping areas.

5. The method for removing grid-like shadows from spliced ​​images based on consistency of overlapping areas according to claim 1, characterized in that: The optimal correction coefficient is determined by minimizing the sum of grayscale value errors of corresponding pixels in all overlapping areas, and the basic vignetting correction model is dynamically adjusted according to the optimal correction coefficient to obtain an updated correction model. The specific steps include: The total gray value error of the corresponding pixels in all overlapping areas and the minimum correction coefficient are selected as the optimal correction coefficient, and the basic vignetting correction model is updated to obtain an updated correction model.

6. The method for removing grid-like shadows from spliced ​​images based on consistency of overlapping areas according to claim 5, characterized in that: The calculation formula for vignetting correction of an image by combining several groups of correction coefficients within the value range with the basic vignetting correction model is as follows: I ds =I+δ s M b Among them, I ds represents the image sequence after vignetting correction by applying the sth group of correction coefficients, I represents the image sequence before vignetting correction, δ s represents the sth group of correction coefficients, s represents the correction coefficient index, M b Represents the basic vignetting correction model.

7. The method for removing grid-like shadows from spliced ​​images based on consistency of overlapping areas according to claim 1, characterized in that: The calculation formula for correcting the image sequence to be stitched using the updated correction model is as follows: I d =I+M Among them, I d represents the corrected image sequence, I represents the image sequence before correction, M represents the updated correction model and M = δM b , δ represents the optimal correction coefficient, M b Represents the basic vignetting correction model.

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