Distortion correction method for intelligently identifying image of SWIT6 millimeter lens

Abstract

The invention discloses a distortion correction method for intelligently identifying an image of a SWIT6 millimeter lens. The method comprises the following steps: S1, acquiring original image data through the SWIT6 millimeter lens; s2, calculating a distortion compensation factor in the original image data according to the optical system characteristics of the SWIT6 millimeter lens; s3, correcting geometric distortion by using the distortion compensation factor to obtain image data after geometric correction; s4, according to the optical system characteristics of the SWIT6 millimeter lens, carrying out geometric correction on the edge region of the image data after geometric correction to obtain corrected image data; s5, applying an adaptive denoising algorithm to the corrected image data, dynamically adjusting the noise suppression intensity according to the distribution condition of optical distortion, and performing detail enhancement processing; and S6, outputting the image data after distortion correction, denoising and detail enhancement. The method has the advantages of good detail retention, efficient real-time processing capability and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of image correction, and in particular to a method for correcting the distortion of an image of an intelligently identified SWIR 6mm lens. Background Art

[0002] With the widespread application of short-wave infrared (SWIR) imaging technology, especially in aerospace, military reconnaissance, security monitoring and industrial detection, the advantages of SWIR lenses in low light and complex environments are increasingly valued. However, with the increase in demand, traditional SWIR lens technology often faces image distortion problems in the process of high-resolution and large field of view imaging, especially under large field of view and short focal length conditions, the distortion problem is more serious, especially the geometric distortion and aberration effects in the edge area, which seriously affect the imaging quality and image accuracy.

[0003] In the prior art, although some methods have been proposed to reduce and correct the geometric distortion problem of SWIR lenses, these methods still have many defects and limitations and cannot meet the challenges of higher performance requirements. Specifically, the existing methods have obvious shortcomings in the following aspects:

[0004] 1. Insufficient accuracy of distortion correction: Traditional methods mainly reduce distortion through physical optical design optimization. Although it can effectively reduce some distortion, under the conditions of large field of view and short focal length, it is still difficult to completely eliminate the geometric distortion and aberration in the edge area, and the imaging accuracy is difficult to guarantee.

[0005] 2. Poor adaptability of denoising algorithms: Existing noise suppression methods are usually based on global noise estimation to suppress high-frequency noise in images. However, these methods lack adaptive adjustment of local image areas and distortion compensation areas, making it difficult to cope with complex image content. The denoising effect is not ideal and may affect the retention of details.

[0006] 3. Insufficient detail enhancement capabilities: Traditional methods mainly rely on simple image processing techniques for denoising and restoration, but lack the enhancement of local details, resulting in serious loss of image details after denoising, especially in high-contrast areas of the image, affecting image quality and information accuracy.

[0007] 4. Slow processing speed: Many existing image distortion correction and noise suppression methods rely on cumbersome calculation steps, resulting in insufficient real-time processing capabilities and unable to meet the requirements of fast response and efficient processing, limiting their widespread use in practical applications.

[0008] Therefore, how to provide a distortion correction method for intelligently identifying images from a SWIR 6 mm lens is an urgent problem that those skilled in the art need to solve. Summary of the invention

[0009] One object of the present invention is to provide a method for intelligently identifying distortion correction of images from a SWIR 6mm lens. The method effectively solves the problems of insufficient distortion correction accuracy, incomplete noise suppression, and detail loss during SWIR lens imaging by dynamically adjusting the distortion compensation factor, using a weighted average algorithm to correct pixel coordinates, applying an adaptive denoising algorithm to process noise, and combining detail enhancement technology. The method has the advantages of high-precision image correction capability, excellent noise removal effect, good detail retention, and efficient real-time processing capability.

[0010] According to an embodiment of the present invention, a method for intelligently identifying distortion correction of an image of a SWIR 6 mm lens comprises the following steps:

[0011] S1, acquiring raw image data through a SWIR 6 mm lens, and performing distortion analysis on the acquired raw image data to identify geometric distortion in the raw image data;

[0012] S2. Calculate the distortion compensation factor in the original image data according to the optical system characteristics of the SWIR 6 mm lens, including focal length, aperture and optical system configuration, establish a mathematical model for distortion correction, and apply the mathematical model to the original image data to generate corrected image data;

[0013] S3, using the distortion compensation factor and a geometric transformation algorithm to adjust the pixel coordinates in the corrected image data, correct the geometric distortion, and obtain geometrically corrected image data;

[0014] S4, according to the optical system characteristics of the SWIR 6 mm lens, geometrically correcting the edge area of ​​the geometrically corrected image data to reduce the geometric distortion caused by the edge effect of the optical system, and obtaining the corrected image data;

[0015] S5. Apply an adaptive denoising algorithm to the corrected image data, dynamically adjust the noise suppression strength according to the distribution of optical distortion, eliminate the noise introduced by the correction, and perform detail enhancement processing;

[0016] S6. Output the image data after distortion correction, denoising and detail enhancement.

[0017] Optionally, the S1 specifically includes:

[0018] S11, acquiring raw image data through a SWIR 6 mm lens, wherein the raw image data is a short-wave infrared image in a wavelength range of 900 nm to 1700 nm, and the raw image data is in a two-dimensional matrix form, including pixel position and gray value information;

[0019] S12, preprocessing the acquired original image data, including cropping, image scaling, and grayscale value normalization, to obtain standard image data;

[0020] S13. According to the optical design parameters of the SWIR 6 mm lens, an optical distortion model is established, and geometric distortion analysis is performed on the pre-processed standard image data, including radial distortion analysis and tangential distortion analysis;

[0021] Radial distortion analysis: ;

[0022] in, is the radial distortion value, is the distance from the pixel in the standard image data to the optical axis, , , is the radial distortion coefficient;

[0023] Tangential distortion analysis: ;

[0024] in, is the tangential distortion value, , is the coordinate of the pixel point in the standard image data, , , is the tangential distortion coefficient;

[0025] S14. Using a feature point matching algorithm, identify and extract pixel positions in the original image data corresponding to feature points in the standard image data, and quantify the geometric distortion in the original image data by comparing their position deviations.

[0026] Optionally, the S2 specifically includes:

[0027] S21. According to the optical system characteristics of the SWIR 6 mm lens, obtain the focal length, aperture, lens curvature, refractive index of optical elements and relative positions of the optical elements;

[0028] S22, establishing an optical transfer function of the lens according to the focal length, aperture, lens curvature and refractive index of the optical element: ;

[0029] in, is the spatial frequency corresponding to The optical transfer function, is the cutoff frequency of the lens, is the attenuation index of the lens, is the spatial frequency in the frequency domain of the standard image data;

[0030] S23. Calculate the distortion compensation factor in the original image data based on the optical transfer function , the distortion compensation factor is used to correct the geometric distortion in the original image data: ;

[0031] in, is the distortion compensation factor, is the optical transfer function, is the distance from the pixel in the standard image data to the optical axis, , , is the radial distortion coefficient;

[0032] S24. Based on the calculated distortion compensation factor, a mathematical model for distortion correction is established in combination with the optical transfer function of the SWIR 6 mm lens and the geometric characteristics of the original image data: ;

[0033] in, is the corrected image data, is the original image data, is the distortion compensation factor;

[0034] S25, applying the mathematical model for distortion correction to the original image data, performing geometric distortion correction on the original image data, and generating corrected image data.

[0035] Optionally, the S3 specifically includes:

[0036] S31. Using distortion compensation factors , adjusting the pixel coordinates in the corrected image data, wherein the adjustment operation includes transforming the coordinates of each pixel point in the image data to correct the geometric distortion;

[0037] S32, according to the distortion compensation factor and the original pixel coordinates of the rectified image data , the pixel coordinate transformation formula is established through the geometric transformation algorithm: ; ;

[0038] in, and is the pixel coordinate after geometric correction, and is the original pixel coordinate of the rectified image data, and is the pixel coordinate adjustment amount, is the distortion compensation factor;

[0039] S33, based on the distortion compensation factor of each pixel in the corrected image data , use the weighted average algorithm to calculate the pixel coordinate adjustment amount of each pixel: ; ;

[0040] in, is the weighting factor: ;

[0041] in, and are the coordinates of adjacent pixels, is the distortion compensation factor of adjacent pixels, is a small constant used to avoid the weighting factor being zero;

[0042] S34, adjusting all pixel points in the image data through a coordinate transformation formula and a weighted average algorithm to eliminate geometric distortion;

[0043] S35, recalculating the corrected image data using a bilinear interpolation algorithm according to the adjusted pixel coordinates;

[0044] S36. Output the geometrically corrected image data.

[0045] Optionally, the S4 specifically includes:

[0046] S41, identifying an edge region in the geometrically corrected image data according to the optical system characteristics of the SWIR 6 mm lens, wherein the edge region is a region within a range of 10 to 20 pixels from an edge of the image in the geometrically corrected image data;

[0047] S42, calculating the edge area distortion compensation factor according to the geometric characteristics of the geometrically corrected image data , the edge area distortion compensation factor is used to correct the geometric distortion caused by the edge effect of the optical system: ;

[0048] in, is the edge area distortion compensation factor, and is the original pixel coordinate of the rectified image data, is the distance from the pixel to the lens optical axis, is the coefficient for controlling the compensation intensity;

[0049] S43, adjusting the pixel coordinates of the edge area using a geometric transformation algorithm according to the edge area distortion compensation factor;

[0050] S44, recalculating the pixel values ​​of the edge area using a bilinear interpolation method according to the adjusted pixel coordinates;

[0051] S45, correcting all pixels in the entire image to eliminate geometric distortion caused by edge effects of the optical system;

[0052] S46, outputting the image data after geometric correction and edge area correction.

[0053] Optionally, the S5 specifically includes:

[0054] S51, performing noise analysis on the corrected image data, wherein the noise analysis includes identifying noise introduced into the image due to the distortion correction process;

[0055] S52. Dynamically adjust the noise suppression intensity in the denoising algorithm based on the distribution of local optical distortion of the corrected image data : ;

[0056] in, is the noise suppression strength, is the adjustment factor, is the distortion compensation factor for each pixel;

[0057] S53, applying an adaptive denoising algorithm based on the adjusted noise suppression strength to suppress the noise in the corrected image data through local noise estimation to obtain denoised image data;

[0058] S54, after the denoising process, detail enhancement is performed based on the local detail information of the denoised image data, using an enhancement coefficient Adjust the texture detail of an image: ;

[0059] in, is the image data after detail enhancement. is the denoised image data, is the gradient value of the image data at the pixel point, is the detail enhancement factor;

[0060] S55, outputting the image data after the denoising and detail enhancement processing.

[0061] Optionally, the S53 specifically includes:

[0062] S531, applying the noise suppression strength based on the adjustment An adaptive denoising algorithm, wherein the denoising algorithm comprises denoising the corrected image data by a wavelet transform method;

[0063] S532, using wavelet transform to decompose the image data, and convert the corrected image data The low-pass filter and high-pass filter are used to obtain the low-frequency subband and high-frequency subband respectively: ; ;

[0064] in, is the low-frequency subband of the image data, is the high frequency subband of the image data, and They are low-pass filtering and high-pass filtering operations respectively;

[0065] S533, according to the noise suppression strength , dynamically adjust the high frequency sub-band The noise figure in: ;

[0066] in, is the adjusted high frequency subband, is the original high frequency subband, is the noise suppression strength;

[0067] S534, reconstructing the adjusted low-frequency and high-frequency sub-bands through inverse wavelet transform to obtain denoised image data : ;

[0068] in, is the denoised image data, is the low frequency subband, is the adjusted high frequency subband;

[0069] S535: Output the denoised image data.

[0070] The beneficial effects of the present invention are:

[0071] (1) The present invention successfully solves the problems of geometric distortion, noise suppression and detail loss in SWIR6mm lens images by combining dynamic distortion compensation factor, adaptive denoising algorithm, weighted average algorithm and detail enhancement technology, thereby significantly improving the quality and accuracy of the image. This method can effectively eliminate edge distortion in large field of view and short focal length imaging, ensure high resolution and accuracy of the image, while maintaining the accuracy of image details and information.

[0072] (2) The present invention accurately removes the noise caused by geometric correction, especially high-frequency noise, by adaptively adjusting the noise suppression strength and applying the wavelet transform denoising method, significantly improving the quality of image data, especially under low light conditions. This method retains more image details after denoising and further improves the usability of the image by detail enhancement, which is particularly suitable for SWIR imaging in high resolution and complex environments.

[0073] (3) The present invention provides comprehensive optimization of SWIR lens imaging quality by combining multiple processing steps of geometric correction and adaptive denoising, significantly improving processing speed and real-time response capability. This enables the method to be widely used in real-time image processing tasks with high-precision imaging requirements, especially in aerospace, security monitoring and industrial detection, with the advantages of high efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0075] Figure 1 This is an overall flow chart of a method for intelligently identifying distortion correction of images from a SWIR 6mm lens proposed by the present invention. DETAILED DESCRIPTION

[0076] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0077] refer to Figure 1 , a method for intelligently identifying distortion correction of images of a SWIR 6 mm lens, comprising the following steps:

[0078] S1, acquiring raw image data through a SWIR 6 mm lens, and performing distortion analysis on the acquired raw image data to identify geometric distortion in the raw image data;

[0079] In this implementation, S1 specifically includes:

[0080] S11, acquiring raw image data through a SWIR 6 mm lens, wherein the raw image data is a short-wave infrared image in a wavelength range of 900 nm to 1700 nm, and the raw image data is in a two-dimensional matrix form, including pixel position and gray value information;

[0081] S12, preprocessing the acquired original image data, including cropping, image scaling, and grayscale value normalization, to obtain standard image data;

[0082] S13. According to the optical design parameters of the SWIR 6 mm lens, an optical distortion model is established, and geometric distortion analysis is performed on the pre-processed standard image data, including radial distortion analysis and tangential distortion analysis;

[0083] Radial distortion analysis: ;

[0084] in, is the radial distortion value, is the distance from the pixel point in the standard image data to the optical axis, , , is the radial distortion coefficient;

[0085] Tangential distortion analysis: ;

[0086] in, is the tangential distortion value, , is the coordinate of the pixel point in the standard image data, , , is the tangential distortion coefficient;

[0087] S14. Using a feature point matching algorithm, identify and extract pixel positions in the original image data corresponding to feature points in the standard image data, and quantify the geometric distortion in the original image data by comparing their position deviations.

[0088] This implementation successfully identifies the geometric distortion problem in the image by using a SWIR 6mm lens to obtain the original image data and performing distortion analysis on the obtained original image data. This method can accurately identify the distortion features in the image, especially the edge distortion caused by large field of view and short focal length imaging, greatly improving the accuracy of distortion detection.

[0089] S2. Calculate the distortion compensation factor in the original image data according to the optical system characteristics of the SWIR 6 mm lens, including focal length, aperture and optical system configuration, establish a mathematical model for distortion correction, and apply the mathematical model to the original image data to generate corrected image data;

[0090] In this implementation, S2 specifically includes:

[0091] S21. According to the optical system characteristics of the SWIR 6 mm lens, obtain the focal length, aperture, lens curvature, refractive index of optical elements and relative positions of the optical elements;

[0092] S22, establishing an optical transfer function of the lens according to the focal length, aperture, lens curvature and refractive index of the optical element: ;

[0093] in, is the spatial frequency corresponding to The optical transfer function, is the cutoff frequency of the lens, is the attenuation index of the lens, is the spatial frequency in the frequency domain of the standard image data;

[0094] S23. Calculate the distortion compensation factor in the original image data based on the optical transfer function , the distortion compensation factor is used to correct the geometric distortion in the original image data: ;

[0095] in, is the distortion compensation factor, is the optical transfer function, is the distance from the pixel point in the standard image data to the optical axis, , , is the radial distortion coefficient;

[0096] S24. Based on the calculated distortion compensation factor, a mathematical model for distortion correction is established in combination with the optical transfer function of the SWIR 6 mm lens and the geometric characteristics of the original image data: ;

[0097] in, is the corrected image data, is the original image data, is the distortion compensation factor;

[0098] S25, applying the mathematical model for distortion correction to the original image data, performing geometric distortion correction on the original image data, and generating corrected image data.

[0099] This embodiment combines the optical system characteristics of the SWIR 6mm lens to calculate the distortion compensation factor in the original image data and establish a mathematical model for distortion correction. This method can accurately quantify the geometric distortion in the image according to the optical characteristics of the lens, and effectively correct the original image data through the mathematical model to generate high-quality corrected image data.

[0100] S3, using the distortion compensation factor and a geometric transformation algorithm to adjust the pixel coordinates in the corrected image data, correct the geometric distortion, and obtain geometrically corrected image data;

[0101] In this implementation, S3 specifically includes:

[0102] S31. Using distortion compensation factors , adjusting the pixel coordinates in the corrected image data, wherein the adjustment operation includes transforming the coordinates of each pixel point in the image data to correct the geometric distortion;

[0103] S32, according to the distortion compensation factor and the original pixel coordinates of the rectified image data , the pixel coordinate transformation formula is established through the geometric transformation algorithm: ; ;

[0104] in, and is the pixel coordinate after geometric correction, and is the original pixel coordinate of the rectified image data, and is the pixel coordinate adjustment amount, is the distortion compensation factor;

[0105] S33, based on the distortion compensation factor of each pixel in the corrected image data , use the weighted average algorithm to calculate the pixel coordinate adjustment amount of each pixel: ; ;

[0106] in, is the weighting factor: ;

[0107] in, and are the coordinates of adjacent pixels, is the distortion compensation factor of adjacent pixels, is a small constant used to avoid the weighting factor being zero;

[0108] S34, adjusting all pixel points in the image data through a coordinate transformation formula and a weighted average algorithm to eliminate geometric distortion;

[0109] S35, recalculating the corrected image data using a bilinear interpolation algorithm according to the adjusted pixel coordinates;

[0110] S36. Output the geometrically corrected image data.

[0111] This embodiment uses the distortion compensation factor and the geometric transformation algorithm to adjust the pixel coordinates in the corrected image data, accurately corrects the geometric distortion, and obtains the geometrically corrected image data. This method can effectively eliminate the image distortion caused by lens distortion, especially the geometric deformation in edge areas and complex scenes.

[0112] S4, according to the optical system characteristics of the SWIR 6 mm lens, geometrically correcting the edge area of ​​the geometrically corrected image data to reduce the geometric distortion caused by the edge effect of the optical system, and obtaining the corrected image data;

[0113] In this implementation, S4 specifically includes:

[0114] S41, identifying an edge region in the geometrically corrected image data according to the optical system characteristics of the SWIR 6 mm lens, wherein the edge region is a region within a range of 10 to 20 pixels from an edge of the image in the geometrically corrected image data;

[0115] S42, calculating the edge area distortion compensation factor according to the geometric characteristics of the geometrically corrected image data , the edge area distortion compensation factor is used to correct the geometric distortion caused by the edge effect of the optical system: ;

[0116] in, is the edge area distortion compensation factor, and is the original pixel coordinate of the rectified image data, is the distance from the pixel to the lens optical axis, is the coefficient for controlling the compensation intensity;

[0117] S43, adjusting the pixel coordinates of the edge area using a geometric transformation algorithm according to the edge area distortion compensation factor;

[0118] S44, recalculating the pixel values ​​of the edge area using a bilinear interpolation method according to the adjusted pixel coordinates;

[0119] S45, correcting all pixels in the entire image to eliminate geometric distortion caused by edge effects of the optical system;

[0120] S46, outputting the image data after geometric correction and edge area correction.

[0121] This embodiment combines the optical system characteristics of the SWIR6mm lens to accurately correct the edge area of ​​the geometrically corrected image data, significantly reducing the geometric distortion caused by the edge effect of the optical system. Through this process, the distortion of the image edge can be effectively repaired, and the overall accuracy and clarity of the image can be improved, especially in the edge area of ​​the image.

[0122] S5. Apply an adaptive denoising algorithm to the corrected image data, dynamically adjust the noise suppression strength according to the distribution of optical distortion, eliminate the noise introduced by the correction, and perform detail enhancement processing;

[0123] In this implementation, S5 specifically includes:

[0124] S51, performing noise analysis on the corrected image data, wherein the noise analysis includes identifying noise introduced into the image due to the distortion correction process;

[0125] S52. Dynamically adjust the noise suppression intensity in the denoising algorithm based on the distribution of local optical distortion of the corrected image data : ;

[0126] in, is the noise suppression strength, is the adjustment factor, is the distortion compensation factor for each pixel;

[0127] S53, applying an adaptive denoising algorithm based on the adjusted noise suppression strength to suppress the noise in the corrected image data through local noise estimation to obtain denoised image data;

[0128] S54, after the denoising process, detail enhancement is performed based on the local detail information of the denoised image data, using an enhancement coefficient Adjust the texture detail of an image: ;

[0129] in, is the image data after detail enhancement. is the denoised image data, is the gradient value of the image data at the pixel point, is the detail enhancement factor;

[0130] S55, outputting the image data after the denoising and detail enhancement processing.

[0131] The S53 specifically includes:

[0132] S531, applying the noise suppression strength based on the adjustment An adaptive denoising algorithm, wherein the denoising algorithm comprises denoising the corrected image data by a wavelet transform method;

[0133] S532, using wavelet transform to decompose the image data, and convert the corrected image data The low-pass filter and high-pass filter are used to obtain the low-frequency subband and high-frequency subband respectively: ; ;

[0134] in, is the low-frequency subband of the image data, is the high frequency subband of the image data, and They are low-pass filtering and high-pass filtering operations respectively;

[0135] S533, according to the noise suppression strength , dynamically adjust the high frequency sub-band The noise figure in: ;

[0136] in, is the adjusted high frequency subband, is the original high frequency subband, is the noise suppression strength;

[0137] S534, reconstructing the adjusted low-frequency and high-frequency sub-bands through inverse wavelet transform to obtain denoised image data : ;

[0138] in, is the denoised image data, is the low frequency subband, is the adjusted high frequency subband;

[0139] S535: Output the denoised image data.

[0140] This implementation method uses an adaptive denoising algorithm to dynamically adjust the noise suppression strength according to the distribution of optical distortion, successfully eliminates the noise introduced by distortion correction, and performs detail enhancement on the basis of denoising. This method can effectively remove noise from image data, especially high-frequency noise generated during the correction process, while maintaining and improving image details, ensuring the smoothness and clarity of image quality.

[0141] S6. Output the image data after distortion correction, denoising and detail enhancement.

[0142] This embodiment successfully improves the quality and accuracy of the image by outputting image data after distortion correction, denoising and detail enhancement. This method can effectively eliminate image distortion caused by lens distortion, noise and other influencing factors, while retaining and enhancing the detail information in the image.

[0143] Example:

[0144] In order to verify the effectiveness of the present invention, the technology was selected to be applied in an aerospace mission, which was performed by a high-resolution satellite dedicated to earth observation. The mission of the satellite is to conduct large-scale ground monitoring to assist in resource exploration, environmental monitoring and disaster prediction. The SWIR6mm lens carried by the satellite must meet the imaging requirements of high resolution and large field of view, so it has extremely high requirements for imaging quality. Especially in the remote sensing imaging process, distortion and noise will seriously affect the quality and accuracy of the data, and then affect subsequent data analysis and decision-making. Therefore, it is particularly important to apply the image distortion correction method proposed in the present invention to improve the quality and accuracy of the image.

[0145] The satellite mission team first used a SWIR 6mm lens to conduct large-area ground observations and obtained raw image data. Since the lens adopts a wide-angle short-focus design, the geometric distortion in the image, especially in the edge area, is very obvious, which makes the image unable to be directly used for accurate analysis. In this process, the traditional image correction method cannot effectively solve the distortion problem under the conditions of large field of view and short focal length, resulting in the image quality failing to meet actual needs. Therefore, the satellite mission team decided to use the image distortion correction method proposed in the present invention for processing.

[0146] First, the geometric distortion of the original image data is identified through distortion analysis, especially the edge area of ​​the image. Through the distortion compensation factor calculation and geometric transformation algorithm of the present invention, the geometric distortion of the image is effectively corrected. The coordinates of each pixel point are accurately adjusted. After geometric correction, the distortion in the image is significantly reduced, and the geometric accuracy of the image is greatly improved.

[0147] Next, an adaptive denoising algorithm was used to address the noise introduced by distortion correction in the image. Based on the noise distribution in each region, the noise suppression intensity was dynamically adjusted, and wavelet transform was applied to suppress the noise in the image data, successfully removing high-frequency noise. At the same time, the denoised image was further optimized using a detail enhancement algorithm, and by enhancing the gradient information of the image, the details were effectively retained.

[0148] After this series of processing, the generated image data was evaluated in real time. Data analysis showed that the corrected image had significant improvements in clarity, detail fidelity, and geometric accuracy. Especially in the edge area, after geometric correction, image distortion was effectively eliminated, and the image quality was uniform and accurate. The specific data results are as follows:

[0149] Table 1: Image quality assessment report

[0150] From the data in Table 1, it can be seen that after implementing the method of the present invention, the degree of image distortion is significantly reduced, the noise is effectively removed, the image details are fully enhanced, and the resolution and clarity of the image are significantly improved. In specific application scenarios, the use of this technology can significantly improve the image quality and meet the satellite's requirements for high-resolution, low-distortion imaging.

[0151] The present invention successfully solves the problems of distortion, noise and detail loss faced by existing SWIR lenses in high-resolution and large field-of-view imaging by combining dynamic distortion compensation factors, adaptive denoising algorithms and detail enhancement techniques. Through this method, satellites can provide more accurate and reliable image data, greatly improving the image analysis capabilities in resource exploration, environmental monitoring and other fields. Specifically, this method not only significantly improves the geometric accuracy and clarity of the image, but also effectively removes noise and enhances the details in the image, providing more accurate support for subsequent remote sensing image processing and analysis.

[0152] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for intelligently identifying distortion correction of images from a SWIR 6mm lens, characterized in that: The steps include: S1, acquiring raw image data through a SWIR 6 mm lens, and performing distortion analysis on the acquired raw image data to identify geometric distortion in the raw image data; S2. Calculate the distortion compensation factor in the original image data according to the optical system characteristics of the SWIR 6 mm lens, including focal length, aperture and optical system configuration, establish a mathematical model for distortion correction, and apply the mathematical model to the original image data to generate corrected image data; S3, using the distortion compensation factor and a geometric transformation algorithm to adjust the pixel coordinates in the corrected image data, correct the geometric distortion, and obtain geometrically corrected image data; S4, according to the optical system characteristics of the SWIR 6 mm lens, geometrically correcting the edge area of ​​the geometrically corrected image data to reduce the geometric distortion caused by the edge effect of the optical system, and obtaining the corrected image data; S5. Apply an adaptive denoising algorithm to the corrected image data, dynamically adjust the noise suppression strength according to the distribution of optical distortion, eliminate the noise introduced by the correction, and perform detail enhancement processing; S6. Output the image data after distortion correction, denoising and detail enhancement.

2. The method for intelligently identifying the distortion correction of images of a SWIR 6 mm lens according to claim 1, characterized in that: The S1 specifically includes: S11, acquiring raw image data through a SWIR 6 mm lens, wherein the raw image data is a short-wave infrared image in a wavelength range of 900 nm to 1700 nm, and the raw image data is in a two-dimensional matrix form, including pixel position and gray value information; S12, preprocessing the acquired original image data, including cropping, image scaling, and grayscale value normalization, to obtain standard image data; S13. According to the optical design parameters of the SWIR 6 mm lens, an optical distortion model is established, and geometric distortion analysis is performed on the pre-processed standard image data, including radial distortion analysis and tangential distortion analysis; Radial distortion analysis: ； in, is the radial distortion value, is the distance from the pixel point in the standard image data to the optical axis, , , is the radial distortion coefficient; Tangential distortion analysis: ； in, is the tangential distortion value, , is the coordinate of the pixel point in the standard image data, , , is the tangential distortion coefficient; S14. Using a feature point matching algorithm, identify and extract pixel positions in the original image data corresponding to feature points in the standard image data, and quantify the geometric distortion in the original image data by comparing their position deviations.

3. The method for intelligently identifying the distortion correction of images of a SWIR 6 mm lens according to claim 1, characterized in that: The S2 specifically includes: S21. According to the optical system characteristics of the SWIR 6 mm lens, obtain the focal length, aperture, lens curvature, refractive index of optical elements and relative positions of the optical elements; S22, establishing an optical transfer function of the lens according to the focal length, aperture, lens curvature and refractive index of the optical element: ； in, is the spatial frequency corresponding to The optical transfer function, is the cutoff frequency of the lens, is the attenuation index of the lens, is the spatial frequency in the frequency domain of the standard image data; S23. Calculate the distortion compensation factor in the original image data based on the optical transfer function , the distortion compensation factor is used to correct the geometric distortion in the original image data: ； in, is the distortion compensation factor, is the optical transfer function, is the distance from the pixel point in the standard image data to the optical axis, , , is the radial distortion coefficient; S24. Based on the calculated distortion compensation factor, a mathematical model for distortion correction is established in combination with the optical transfer function of the SWIR 6 mm lens and the geometric characteristics of the original image data: ； in, is the corrected image data, is the original image data, is the distortion compensation factor; S25, applying the mathematical model for distortion correction to the original image data, performing geometric distortion correction on the original image data, and generating corrected image data.

4. The method for intelligently identifying the distortion correction of images of a SWIR 6 mm lens according to claim 1, characterized in that: The S3 specifically includes: S31. Using distortion compensation factors , adjusting the pixel coordinates in the corrected image data, wherein the adjustment operation includes transforming the coordinates of each pixel point in the image data to correct the geometric distortion; S32, according to the distortion compensation factor and the original pixel coordinates of the rectified image data , the pixel coordinate transformation formula is established through the geometric transformation algorithm: ； ； in, and is the pixel coordinate after geometric correction, and is the original pixel coordinate of the rectified image data, and is the pixel coordinate adjustment amount, is the distortion compensation factor; S33, based on the distortion compensation factor of each pixel in the corrected image data , use the weighted average algorithm to calculate the pixel coordinate adjustment amount of each pixel: ； ； in, is the weighting factor: ； in, and are the coordinates of adjacent pixels, is the distortion compensation factor of adjacent pixels, is a small constant used to avoid the weighting factor being zero; S34, adjusting all pixel points in the image data through a coordinate transformation formula and a weighted average algorithm to eliminate geometric distortion; S35, recalculating the corrected image data using a bilinear interpolation algorithm according to the adjusted pixel coordinates; S36. Output the geometrically corrected image data.

5. The method for correcting the distortion of an image of a SWIR 6 mm lens according to claim 1, characterized in that: The S4 specifically includes: S41, identifying an edge region in the geometrically corrected image data according to the optical system characteristics of the SWIR 6 mm lens, wherein the edge region is a region within a range of 10 to 20 pixels from an edge of the image in the geometrically corrected image data; S42, calculating the edge area distortion compensation factor according to the geometric characteristics of the geometrically corrected image data , the edge area distortion compensation factor is used to correct the geometric distortion caused by the edge effect of the optical system: ； in, is the edge area distortion compensation factor, and is the original pixel coordinate of the rectified image data, is the distance from the pixel to the lens optical axis, is the coefficient for controlling the compensation intensity; S43, adjusting the pixel coordinates of the edge area using a geometric transformation algorithm according to the edge area distortion compensation factor; S44, recalculating the pixel values ​​of the edge area using a bilinear interpolation method according to the adjusted pixel coordinates; S45, correcting all pixels in the entire image to eliminate geometric distortion caused by edge effects of the optical system; S46, outputting the image data after geometric correction and edge area correction.

6. The method for intelligently identifying the distortion correction of images of a SWIR 6 mm lens according to claim 1, characterized in that: The S5 specifically includes: S51, performing noise analysis on the corrected image data, wherein the noise analysis includes identifying noise introduced into the image due to the distortion correction process; S52. Dynamically adjust the noise suppression intensity in the denoising algorithm based on the distribution of local optical distortion of the corrected image data : ； in, is the noise suppression strength, is the adjustment factor, is the distortion compensation factor for each pixel; S53, applying an adaptive denoising algorithm based on the adjusted noise suppression strength to suppress the noise in the corrected image data through local noise estimation to obtain denoised image data; S54, after the denoising process, detail enhancement is performed based on the local detail information of the denoised image data, using an enhancement coefficient Adjust the texture detail of an image: ； in, is the image data after detail enhancement. is the denoised image data, is the gradient value of the image data at the pixel point, is the detail enhancement factor; S55, outputting the image data after the denoising and detail enhancement processing.

7. The method for intelligently identifying the distortion correction of images of a SWIR 6 mm lens according to claim 6, characterized in that: The S53 specifically includes: S531, applying the noise suppression strength based on the adjustment An adaptive denoising algorithm, wherein the denoising algorithm comprises denoising the corrected image data by a wavelet transform method; S532, using wavelet transform to decompose the image data, and convert the corrected image data The low-pass filter and high-pass filter are used to obtain the low-frequency subband and high-frequency subband respectively: ； ； in, is the low-frequency subband of the image data, is the high frequency subband of the image data, and They are low-pass filtering and high-pass filtering operations respectively; S533, according to the noise suppression strength , dynamically adjust the high frequency sub-band The noise figure in: ； in, is the adjusted high frequency subband, is the original high frequency subband, is the noise suppression strength; S534, reconstructing the adjusted low-frequency and high-frequency sub-bands through inverse wavelet transform to obtain denoised image data : ； in, is the denoised image data, is the low frequency subband, is the adjusted high frequency subband; S535: Output the denoised image data.

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