A collaborative correction method for imaging distortion and dispersion in a rotating bi-prism human eye-mimicking system

Abstract

The present invention relates to a collaborative correction method for imaging distortion and dispersion of a rotating bi-prism human eye simulation system. The method comprises the following steps: building a rotating bi-prism human eye simulation system to obtain the original distorted and dispersed image; based on the rectangular boundary of the original field of view R channel, reading in the basic parameters, and n r The deflected field of view boundary is determined through inverse ray tracing, and the coordinates of all integer points within it that need to be filled are obtained, reconstructing the incident light vector. The position and grayscale value of the integer points in the deflected field of view in the original field of view of the distorted image are obtained using a reverse mapping bilinear interpolation technique based on inverse ray tracing. Grayscale values ​​are filled in one by one to correct the rotational biprism imaging distortion of the corresponding channel. Distortion correction is then performed on the B and G channels separately. The distortion-corrected images of the R, G, and B channels are superimposed into an RGB image based on a mask matrix, ultimately completing dispersion correction. This method effectively solves the distortion and dispersion problems.

Description

Technical Field

[0001] The present invention belongs to the technical field of imaging systems and image processing, and in particular relates to a collaborative correction method for imaging distortion and dispersion of a rotating bi-prism human eye-simulating system. Background Art

[0002] The human eye is the primary medium through which humans acquire external information. Its imaging characteristic is that the density of photoreceptor cells decreases as the distance between them and the center of the retina increases. This characteristic makes the acquisition of visual information non-uniform, with high resolution in the fovea of ​​the retina and low resolution in the periphery. Therefore, eye-mimicking imaging can achieve high-resolution imaging of the region of interest while providing a rough representation of the surrounding area, thereby compressing redundant data in the peripheral area. Based on this characteristic, eye-mimicking imaging is suitable for applications with high requirements for both field of view and resolution, and therefore has broad application prospects in target recognition and tracking, intelligent monitoring, biomedicine, and other fields.

[0003] Currently, there are a variety of devices that achieve human eye-like imaging, such as human eye-like sensors with discretely distributed units based on sensor processing technology and human eye-like sensors based on curved lens arrays. However, these devices are relatively complex in structure and, due to limitations in manufacturing processes, their practical application remains difficult. Human eye-like imaging methods based on rotating dual-prism imaging systems have proven their capabilities in expanding the field of view, achieving localized high resolution, and flexibly adjusting high-resolution regions. However, due to the prism's non-uniform refraction of light beams in different wavelength bands, rotating dual-prism imaging systems produce severe imaging distortion and dispersion. To correct for imaging distortion in rotating dual-prism imaging systems, a large number of related studies have been conducted and achieved promising results. For example, ray tracing based on the first-order paraxial approximation and correction using homography, and inverse ray tracing distortion correction methods based on the law of vector refraction, have been used. Furthermore, current methods for eliminating imaging dispersion in rotating dual-prism imaging systems primarily rely on optical system design. For example, these methods involve combining a cemented prism with other materials or etching a grating onto the prism, depending on the spectral range of interest and the intended application. However, these optical system design-based dispersion elimination methods suffer from drawbacks such as bulky structure, high design difficulty, and high cost. Furthermore, while there are numerous image processing methods for eliminating dispersion, such as leveraging pixel correlation between color channels to recover cross-channel information and thereby eliminate dispersion, these algorithms primarily address the dispersion caused by the camera itself, rather than being designed specifically for rotating dual-prism imaging systems. Consequently, they cannot address the imaging distortion that also occurs with dispersion.

[0004] A simplified field of view method for accelerating image processing based on a rotating bi-prism imaging system was proposed in the prior art (Wang P and Huang D, “High-efficiency simplification method of irregular FOV for accelerating the imaging process in the Risley-Prism system,” Opt. Express, 2022, 30(21): 37364-37378.). By simplifying the complete field of view, the process of obtaining all integer points within the simplified field of view is accelerated, thereby achieving efficient imaging distortion correction.

[0005] Prior art (Y.Zhou, S.Fan, G.Liu, Y.Chen, and D.Fan, “Image distortions caused by rotational double prisms and their correction,” Acta Opt. Sin. 35(9), 143–150(2015).) proposed using pixel correlation between color channels to solve the dispersion problem, simplifying the problem to single-channel deblurring, and proving that this method can eliminate dispersion and restore high-quality color images. Although the above two methods each solve the image distortion or dispersion problem, they cannot simultaneously solve the distortion and dispersion problems caused by the rotating double prism. Moreover, the algorithm designed for the imaging dispersion of the camera itself is not effective when directly applied to the degraded image of the rotating double prism imaging system. Summary of the Invention

[0006] The purpose of the present invention is to overcome the above-mentioned defects of the prior art and provide a collaborative correction method for imaging distortion and dispersion of a rotating bi-prism human eye simulation system.

[0007] The principle of the present invention is as follows: Since the non-uniform refraction of light of different wavelengths by the prism causes both imaging distortion and dispersion problems, in order to achieve degraded image correction, the deflected field of view of the R channel is used as the reference field of view. Based on this field of view, the R, G, and B channels are reverse mapped and bilinearly interpolated using the refractive indices of different channels. The three-channel image is then recombined to achieve simultaneous distortion and dispersion correction.

[0008] To achieve the above objectives, the technical solution of the present invention is: a collaborative correction method for imaging distortion and dispersion of a rotating bi-prism human eye simulation system, which utilizes a rotating bi-prism human eye simulation system to obtain the R channel of the distorted and dispersed image as a reference channel, and then performs distortion correction on the R, G, and B channels and then synthesizes them into an RGB image, thereby achieving simultaneous correction of imaging distortion and dispersion.

[0009] In one embodiment of the present invention, the rotating dual-prism human eye-mimicking system includes a single coaxially arranged RGB camera and two wedge-shaped prisms with identical parameters. The two wedge-shaped prisms are coaxially arranged at a predetermined distance and can rotate independently driven by a stepper motor.

[0010] In one embodiment of the present invention, a method for collaboratively correcting imaging distortion and dispersion in a rotating bi-prism human eye-simulating system is implemented in the following steps:

[0011] Step S1: Build a rotating bi-prism human eye system to obtain distortion and dispersion images and calculate the refractive index n r 、n g 、n b ;

[0012] Step S2: Based on the rectangular boundary of the original field of view R channel, read the rotation angle of the bi-prism of the rotating bi-prism human eye system, construct the normal vector of each surface of the bi-prism, and calculate the normal vector of each surface of the bi-prism according to the refractive index n. r , the position of the R channel deflection field boundary in the distortion-free grid is obtained by inverse ray tracing;

[0013] Step S3, obtaining the coordinates of integer points in the R channel deflection field of view;

[0014] Step S4: reconstruct the incident light vector based on the integer points in the distortion-free grid; r , the reverse mapping bilinear interpolation technique based on inverse ray tracing is used to obtain the position and grayscale value of the integer points in the distortion-free grid in the distorted image grid. Finally, the grayscale values ​​are filled one by one into the corresponding non-degraded image grid to achieve the correction of the rotating biprism imaging distortion of the corresponding channel;

[0015] Step S5: perform distortion correction on the G and B channels respectively, use the R channel obtained in step S3 to deflect the integer point coordinates in the field of view, and replace the refractive index in step S4 with n g 、n b , repeat step S4;

[0016] Step S6: Based on the mask matrix, the distortion-corrected images of the three channels R, G, and B are superimposed into an RGB image without color boundaries, thereby finally completing the distortion and dispersion correction.

[0017] In one embodiment of the present invention, in step S1, the refractive index n is calculated according to the Schott formula. r 、n g 、n b , Schott's formula is:

[0018] n 2 =a0+a1λ 2 +a2λ-2 +a3λ -4 +a4λ -6 +a5λ -8

[0019] Where λ is the wavelength corresponding to the refractive index of different bands, n is the refractive index representing the R, G, and B channels, and a0-a5 are coefficients determined by the prism material.

[0020] In one embodiment of the present invention, step S2 is specifically as follows:

[0021] Step S21: Based on the pixel array size and equivalent focal length of the RGB camera in the rotating dual prism human eye simulation system, establish the incident vector of the light emitted from the edge of the pixel array to the prism. And converted to a unit vector. Among them, Represents the coordinates of the pixel point in the pixel array on the image plane, the unit is pixel (pixel), f represents the equivalent focal length, c∈{r,g,b};

[0022] Step S22: Calculate the boundary of the marginal light after it is deflected by the rotating biprism according to the law of vector refraction. The formula is:

[0023]

[0024] Where i∈[1,4] is the number of times the light beam passes through the prism surface, n0=n2=n4=1, represents the refractive index in air, and n1=n3 represents the refractive index n of red, green, and blue light passing through the prism. r 、n g 、n b ; and V i c Represent the direction vectors of the light beam before and after refraction, N i Represents the normal vector of the refractive surface.

[0025] In one embodiment of the present invention, step S4 is specifically as follows:

[0026] Step S41: Construct the reverse incident light vector from the object plane to the image plane according to the integer point coordinate set A. And converted to a unit vector. Among them, represents the coordinates of an integer point in the deflection field of view of the R channel, that is, an integer point in the distortion-free grid, and f represents the equivalent focal length;

[0027] Step S42: Using the V0 calculated in step S41 c ', according to the following formula, the integer points in the undistorted grid are Back-mapping onto the distorted grid to obtain non-integer points In this step, c = r;

[0028]

[0029] Step S43: Based on the bilinear interpolation formula, the grayscale information of the integer points of the distorted grid is used to obtain the non-integer points Grayscale information G;

[0030]

[0031] Where i c 、j c are non-integer point coordinates The integer part of m c 、n c are non-integer point coordinates The fractional part of G(i c ,j c ),G(i c ,j c +1),G(i c +1,j c ),G(i c +1,j c +1) represents the grayscale information of the four integer points closest to the non-integer point in the distorted grid.

[0032] In one embodiment of the present invention, step S5 is specifically as follows:

[0033] Step S51: Using the integer point coordinates of the R channel obtained in step S3, repeat step S4, and replace the refractive index in step S42 with n g , complete the distortion correction of G channel;

[0034] Step S52: Using the integer point coordinates of the R channel obtained in step S3, repeat step S4, replacing the refractive index in step S42 with n b , complete the distortion correction of channel B.

[0035] In one embodiment of the present invention, step S6 is specifically as follows:

[0036] Step S61: The non-integer points in the distorted grid obtained in step S52 are The grayscale information G is assigned to the integer points in the distortion-free grid one by one Get the matrices R, G, B of the corrected images of different channels;

[0037] Step S62: Construct a corresponding mask matrix R for each channel according to the pixel position of the corrected image. mask ,G mask ,B mask, the values ​​of the corresponding positions of the three mask matrices are dot-multiplied to obtain the final mask matrix C representing the intersection of the non-zero areas of the three matrices mask ;

[0038] Step S63, C mask Multiplying with the matrix R, G, and B points respectively generates new corrected images of different channels, which are combined to form an RGB image, thereby achieving distortion and dispersion correction and eliminating boundary color fringes.

[0039] The present invention also provides a collaborative correction system for imaging distortion and dispersion of a rotating dual-prism human eye simulation system, including a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0040] The present invention also provides a computer-readable storage medium on which computer program instructions that can be executed by a processor are stored. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. This paper proposes a collaborative correction method for image distortion and dispersion. This method corrects the RGB channels based on the deflected field of view of the R channel, achieving high-quality imaging. This method enables color imaging without increasing the complexity of the rotating biprism imaging optical path, breaking the wavelength limitations of existing human-eye-mimicking rotating biprism imaging systems. It exhibits significant application potential in imaging reconnaissance, target tracking, intelligent recognition, and other fields.

[0043] 2. The present invention is based on a collaborative correction method for image distortion and dispersion, which effectively solves the problems of distortion and dispersion, and further solves the boundary problem of the field of view, providing high-quality imaging effects for subsequent tasks such as image stitching and multispectral reconstruction based on the corrected image. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a schematic diagram of a rotating bi-prism human eye simulation system provided by an embodiment of the present invention.

[0045] Figure 2 This is a flow chart of a method for collaboratively correcting imaging distortion and dispersion of a rotating bi-prism human eye-mimicking system provided by an embodiment of the present invention.

[0046] Figure 3 This is a distortion and dispersion correction algorithm processing process based on a rotating dual-prism human eye simulation system provided by an embodiment of the present invention.

[0047] Figure 4This is a visualization image of the mask matrix based on the rotating biprism human eye simulation system provided by an embodiment of the present invention, where a, b, and c represent the mask matrices of the R, G, and B channels; and d represents the final mask matrix of the intersection of the non-zero areas of the three matrices.

[0048] Figure 5 Figure 1 shows the results of degraded image correction and color edge removal using a rotating biprism-based human eye simulation system, provided by an embodiment of the present invention. Figure a shows the degraded image captured by the system when θ1 = θ2 = 45°; b shows the corrected image; and c shows the result of color edge fringing removal using a mask template. Figures d, e, and f are magnified images of parts of a, b, and c, respectively.

[0049] Figure 6 This is a comparison of a degraded image and a corrected image of an indoor scene based on a rotating bi-prism human eye simulation system provided by an embodiment of the present invention. The first row is the degraded image, and the second row is the corrected image.

[0050] Figure 7 This is a comparison of a degraded image and a corrected image of an outdoor scene based on a rotating dual-prism human eye simulation system provided by an embodiment of the present invention. The first row is the degraded image, and the second row is the corrected image. DETAILED DESCRIPTION

[0051] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0052] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present application belongs.

[0053] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0054] This invention provides a collaborative correction method for imaging distortion and dispersion in a rotating biprism-like human eye system. Using the rotating biprism-like human eye system, the deflected field of view of the R channel is calculated. Based on this field of view, the R, G, and B channels are reverse-mapped using bilinear interpolation using different refractive indices. A mask matrix is ​​then used to remove color boundaries, thereby achieving distortion and dispersion correction without color fringing. The specific implementation steps of this method are as follows:

[0055] Step S1: Build a rotating bi-prism human eye system to obtain the distorted and dispersed image, i.e. the original degraded image, and calculate the refractive index n according to the Schott formula. r 、n g 、n b ;

[0056] Step S2: Based on the rectangular boundary of the original field of view R channel, read the bi-prism rotation angle, construct the normal vector of each surface of the bi-prism, and calculate the refractive index n. r , the position of the R channel deflection field boundary in the distortion-free grid is obtained by inverse ray tracing;

[0057] Step S3: obtaining the coordinates of all integer points that need to be filled in the deflection field of view in the distortion-free grid according to the four boundaries of the deflection field of view;

[0058] Step S4: reconstruct the incident light vector based on the integer points in the distortion-free grid; r , using the reverse mapping bilinear interpolation technique based on inverse ray tracing to obtain the position and grayscale value of the integer points in the distortion-free grid in the distorted image grid, and finally filling the grayscale values ​​one by one into the corresponding non-degraded image grid to achieve the correction of the rotating biprism imaging distortion of this channel;

[0059] Step S5: perform distortion correction on the G and B channels respectively, use the R channel obtained in step S3 to deflect the integer point coordinates in the field of view, and replace the refractive index in step S4 with n g 、n b , repeat step S4;

[0060] Step S6: Based on the mask matrix, the distortion-corrected images of the three channels R, G, and B are superimposed into an RGB image without color boundaries, thereby finally completing the distortion and dispersion correction.

[0061] The present invention is further described in detail below with reference to the accompanying drawings and examples. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention includes but is not limited to the following embodiments.

[0062] Example

[0063] The present invention discloses a collaborative correction method for imaging distortion and dispersion of a rotating bi-prism human eye-simulating system, wherein the rotating bi-prism human eye-simulating system is composed of two wedge-shaped prisms with completely identical parameters and an ordinary RGB industrial camera. Figure 1As shown in Figure 2, the two prisms are mounted with mechanical structures to ensure coaxiality and spacing, and can rotate independently around the optical axis driven by a stepper motor. To facilitate describing the rotational state and relative position of the two prisms, the rotation angle of the two prisms is defined as the counterclockwise angle between their main cross-sections and the positive x-axis, denoted by (θ1, θ2), respectively.

[0064] In this embodiment, the two prisms have a large vertex angle, and the camera itself has a large field of view. The parameters of the rotating dual-prism human eye simulation system are as follows: the two prism vertex angles α1 = α2 = 11.35°, the prism material is H-K9L, the camera's pixel array size is 3072 × 2048 pixels, and the equivalent focal length f = 5012 pixels.

[0065] refer to Figure 2 This embodiment provides a method for co-correcting distortion and dispersion based on a rotating bi-prism human eye system, comprising the following steps:

[0066] Step S1: Build a rotating bi-prism human eye system to obtain the distorted and dispersed image, i.e. the original degraded image, and calculate the refractive index n according to the Schott formula. r 、n g 、n b ;

[0067] Step S2: Based on the rectangular boundary of the original field of view R channel, read the bi-prism rotation angle, construct the normal vector of each surface of the bi-prism, and calculate the refractive index n. r , the position of the R channel deflection field boundary in the distortion-free grid is obtained by inverse ray tracing;

[0068] Step S3: obtaining the coordinates of all integer points that need to be filled in the deflection field of view in the distortion-free grid according to the four boundaries of the deflection field of view;

[0069] Step S4: reconstruct the incident light vector based on the integer points in the distortion-free grid; r , using the reverse mapping bilinear interpolation technique based on inverse ray tracing to obtain the position and grayscale value of the integer points in the distortion-free grid in the distorted image grid, and finally filling the grayscale values ​​one by one into the corresponding non-degraded image grid to achieve the correction of the rotating biprism imaging distortion of this channel;

[0070] Step S5: perform distortion correction on the G and B channels respectively, use the R channel obtained in step S3 to deflect the integer point coordinates in the field of view, and replace the refractive index in step S4 with n g 、n b , repeat step S4;

[0071] Step S6: Based on the mask matrix, the distortion-corrected images of the three channels R, G, and B are superimposed into an RGB image without color boundaries, thereby finally completing the distortion and dispersion correction.

[0072] In this embodiment, in step S1, the Schott formula is:

[0073] n 2 =a0+a1λ 2 +a2λ -2 +a3λ -4 +a4λ -6 +a5λ -8 (1)

[0074] Where λ is the wavelength corresponding to the refractive index of different bands, n is the refractive index representing the R, G, and B channels, and a0-a5 are coefficients determined by the prism material. According to the formula, n r 、n g 、n b They are 1.516292, 1.519820, and 1.524496 respectively.

[0075] Step S2 is specifically as follows:

[0076] Step S21: According to the pixel array size and equivalent focal length of the RGB camera, establish the incident vector of the light emitted from the edge of the pixel array to the prism And transform it into a unit vector. Among them, represents the coordinates of the pixel point in the pixel array on the image plane, the unit is pixel (pixel), f represents the equivalent focal length, where c∈{r,g,b};

[0077] Step S22: Calculate the boundary of the marginal light after it is deflected by the rotating biprism according to the law of vector refraction. The formula is:

[0078]

[0079] Where i∈[1,4] is the number of times the light beam passes through the prism surface, n0=n2=n4=1, represents the refractive index in air, and n1=n3 represents the refractive index n of red, green, and blue light passing through the prism. r 、n g 、n b . and V i c Represent the direction vectors of the light beam before and after refraction, N i Represents the normal vector of the refractive surface;

[0080] Step S3 is specifically as follows:

[0081] According to the four boundary curves of the deflection field of view, the coordinates of all integer points that need to be filled are obtained;

[0082] Step S4 is specifically as follows:

[0083] Step S41: Construct the reverse incident light vector from the object plane to the image plane according to the integer point coordinate set A. And transform it into a unit vector. Among them, represents the coordinates of an integer point in the deflection field of view of the R channel, that is, an integer point in the distortion-free grid, and f represents the equivalent focal length;

[0084] Step S42: Using the V0 calculated in step S41 c ', according to formula (3), the integer points in the distortion-free grid are Back-mapping onto the distorted grid to obtain non-integer points In this step, c = r, as Figure 3 As shown;

[0085]

[0086] Step S43: Based on the bilinear interpolation formula, the grayscale information on the integer points of the distorted grid is used to obtain the non-integer points Grayscale information G;

[0087]

[0088] Where i c ,j c are non-integer point coordinates The integer part of m c , n c are non-integer point coordinates The fractional part of G(i c ,j c ),G(i c ,j c +1),G(i c +1,j c ),G(i c +1,j c +1) represents the grayscale information of the four integer points closest to the non-integer point in the distorted grid.

[0089] Step S5 is specifically as follows:

[0090] Step S51: Using the integer point coordinates of the R channel obtained in step S3, repeat step S4. The refractive index in step S42 should be replaced by n g , complete the distortion correction of G channel;

[0091] Step S52: Using the integer point coordinates of the R channel obtained in step S3, repeat step S4. The refractive index in step S42 should be replaced by n b , complete the distortion correction of channel B;

[0092] Step S6 is specifically as follows:

[0093] Step S61: The non-integer points in the distorted grid obtained in step S52 are The grayscale information G is assigned to the integer points in the distortion-free grid one by one Get the matrices R, G, B of the corrected images of different channels;

[0094] Step S62: Construct a corresponding mask matrix R for each channel according to the pixel position of the corrected image. mask ,G mask ,B mask , the values ​​of the corresponding positions of the three mask matrices are dot-multiplied to obtain the final mask matrix C representing the intersection of the non-zero areas of the three matrices mask ,like Figure 4 As shown;

[0095] Step S63, C mask Multiplying the matrices R, G, and B respectively produces a new corrected image. Combining these new channels forms an RGB image, which achieves distortion and dispersion correction and eliminates boundary color fringes. Figure 5 shown.

[0096] In this embodiment, steps S4-S6 perform reverse mapping bilinear interpolation processing on the R, G, and B channels based on the deflected field of view of the R channel, construct a mask matrix based on the distortion-corrected channels, eliminate color boundaries, and ultimately achieve distortion and dispersion correction. In order to test the robustness of the algorithm, degraded images with different prism rotation angle combinations are collected for indoor scenes. The two prism rotation angles are kept consistent and are set to θ1=θ2=40°, θ1=θ2=120°, θ1=θ2=210°, and θ1=θ2=330°, respectively. Figure 6 As shown, the first line is the degraded image, and the second line is the corrected image. It can be clearly observed that the dispersion in the degraded image has been well corrected, and the text and other information in the image are also more clearly visible. In addition, indoor scenes are relatively simple and controllable, while outdoor scenes are more complex and changeable. In order to verify the universality of the algorithm, outdoor experiments were also conducted, and the prism rotation angle was changed. The two prism rotation angles were set to θ1=θ2=0°, θ1=θ2=90°, θ1=θ2=180°, θ1=θ2=270° respectively. As shown Figure 7 As shown in the figure, from the correction results, the dispersion phenomenon of scenes such as "buildings", "clouds" and "lake surface" has been significantly improved, and the image quality of the corrected image is significantly better than the degraded image.

[0097] The above embodiments show that the method proposed in the present invention can be based on a rotating dual-prism human eye simulation system and utilize a collaborative correction method to achieve correction of degraded images in various scenes and prism rotation angle combinations, thereby significantly improving image quality.

[0098] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0099] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0100] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0102] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other manner. Any person skilled in the art may utilize the above-disclosed technical content to modify or modify the present invention into equivalent embodiments. However, any simple modifications, equivalent variations, and modifications to the above embodiments that do not depart from the technical content of the present invention and are based on the technical essence of the present invention remain within the scope of protection of the present invention.

Claims

1. A collaborative correction method for imaging distortion and dispersion of a rotating bi-prism human eye-simulating system, characterized in that: Using a rotating bi-prism system to simulate the human eye, the R channel of the distorted and dispersed image is obtained as the reference channel. The R, G, and B channels are then subjected to distortion correction and then synthesized into an RGB image, achieving simultaneous correction of imaging distortion and dispersion. The specific implementation steps of this method are as follows: Step S1: Build a rotating bi-prism human eye system to obtain distortion and dispersion images and calculate the refractive index n r 、n g 、n b ; Step S2: Based on the rectangular boundary of the original field of view R channel, read the rotation angle of the bi-prism of the rotating bi-prism human eye system, construct the normal vector of each surface of the bi-prism, and calculate the normal vector of each surface of the bi-prism according to the refractive index n. r , the position of the R channel deflection field boundary in the distortion-free grid is obtained by inverse ray tracing; Step S3, obtaining the coordinates of integer points in the R channel deflection field of view; Step S4: reconstruct the incident light vector based on the integer points in the distortion-free grid; r , the reverse mapping bilinear interpolation technique based on inverse ray tracing is used to obtain the position and grayscale value of the integer points in the distortion-free grid in the distorted image grid. Finally, the grayscale values ​​are filled one by one into the corresponding non-degraded image grid to achieve the correction of the rotating biprism imaging distortion of the corresponding channel; Step S5: perform distortion correction on the G and B channels respectively, use the R channel obtained in step S3 to deflect the integer point coordinates in the field of view, and replace the refractive index in step S4 with n g 、n b , repeat step S4; Step S6: Based on the mask matrix, the distortion-corrected images of the three channels R, G, and B are superimposed into an RGB image without color boundaries, thereby completing the distortion and dispersion correction. The step S6 is specifically as follows: Step S61: The non-integer points in the distorted grid obtained in step S52 are The grayscale information G is assigned to the integer points in the distortion-free grid one by one Get the matrices R, G, B of the corrected images of different channels; Step S62: Construct a corresponding mask matrix R for each channel according to the pixel position of the corrected image. mask ,G mask ,B mask , the values ​​of the corresponding positions of the three mask matrices are dot-multiplied to obtain the final mask matrix C representing the intersection of the non-zero areas of the three matrices mask ; Step S63, C mask Multiplying with the matrix R, G, and B points respectively generates new corrected images of different channels, which are combined to form an RGB image, thereby achieving distortion and dispersion correction and eliminating boundary color fringes.

2. The collaborative correction method for imaging distortion and dispersion of a rotating bi-prism human eye-simulating system according to claim 1, characterized in that: The rotating double-prism human eye-mimicking system includes a single coaxially arranged RGB camera and two wedge-shaped prisms with exactly the same parameters.

3. The collaborative correction method for imaging distortion and dispersion of a rotating bi-prism human eye-simulating system according to claim 2, characterized in that: The two wedge-shaped prisms are coaxially placed at a predetermined distance and can be independently rotated by a stepping motor.

4. The collaborative correction method for imaging distortion and dispersion of a rotating bi-prism human eye-simulating system according to claim 1, characterized in that: The step S2 is specifically as follows: Step S21: Based on the pixel array size and equivalent focal length of the RGB camera in the rotating dual prism human eye simulation system, establish the incident vector of the light emitted from the edge of the pixel array to the prism. and converted to a unit vector; where, represents the coordinates of the pixel point in the pixel array on the image plane, f represents the equivalent focal length, c∈{r,g,b}; Step S22: Calculate the boundary of the marginal light after it is deflected by the rotating biprism according to the law of vector refraction. The formula is: Where i∈[1,4] is the number of times the light beam passes through the prism surface, n0=n2=n4=1, represents the refractive index in air, and n1=n3 represents the refractive index n of red, green, and blue light passing through the prism. r 、n g 、n b ; and Represent the direction vectors of the light beam before and after refraction, N i Represents the normal vector of the refractive surface.

5. The collaborative correction method for imaging distortion and dispersion of a rotating bi-prism human eye-simulating system according to claim 4, characterized in that: The step S4 is specifically as follows: Step S41: Construct the reverse incident light vector from the object plane to the image plane according to the integer point coordinate set A. and converted to a unit vector; where, represents the coordinates of an integer point in the deflection field of view of the R channel, that is, an integer point in the distortion-free grid, and f represents the equivalent focal length; Step S42: Using the calculated value in step S41 According to the following formula, the integer points in the undistorted grid are Back-mapping onto the distorted grid to obtain non-integer points In this step, c = r; Step S43: Based on the bilinear interpolation formula, the grayscale information of the integer points of the distorted grid is used to obtain the non-integer points Grayscale information G; Where i c 、j c are non-integer point coordinates The integer part of m c 、n c are non-integer point coordinates The fractional part of G(i c ,j c ),G(i c ,j c +1),G(i c +1,j c ),G(i c +1,j c +1) represents the grayscale information of the four integer points closest to the non-integer point in the distorted grid.

6. The collaborative correction method for imaging distortion and dispersion of a rotating bi-prism human eye-simulating system according to claim 5, characterized in that: The step S5 is specifically as follows: Step S51: Using the integer point coordinates of the R channel obtained in step S3, repeat step S4, and replace the refractive index in step S42 with n g , complete the distortion correction of G channel; Step S52: Using the integer point coordinates of the R channel obtained in step S3, repeat step S4, replacing the refractive index in step S42 with n b , complete the distortion correction of channel B.

7. A collaborative correction system for imaging distortion and dispersion of a rotating bi-prism human eye system, characterized in that: The method comprises a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the method steps according to any one of claims 1 to 6 can be implemented.

8. A computer-readable storage medium storing computer program instructions that can be executed by a processor, wherein when the processor executes the computer program instructions, the method steps according to any one of claims 1 to 6 can be implemented.

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