An Adaptive Inversion Method for Arbitrary Polarization Channels with Local Sampling and Alternating Iteration

Abstract

The present invention provides a local sampling and alternating iteration-based adaptive inversion method for any polarization channel, including: Step S1, acquiring polarization channel images; Step S2, estimating global feature information and obtaining a set of local sample points; Step S3, setting an initial analysis angle; Step S4, performing matrix transformation to obtain a multi-polarization channel matrix, an initial polarization angle matrix, and an intermediate analysis angle matrix; Step S5, updating the intermediate polarization angle matrix according to the multi-polarization channel matrix and the intermediate analysis angle matrix and solving to obtain an intermediate polarization angle; Step S6, determining whether the convergence condition is satisfied: if yes, turning to Step S7; if not, performing alternating iteration on Step S4 and Step S5; Step S7, calculating the final analysis angle and reconstructing a degree of polarization image according to the final analysis angle matrix and the multi-polarization channel matrix. The beneficial effect is that the present invention can achieve accurate direction angle inversion and efficient reconstruction of polarization characteristics, improving the accuracy and robustness of the polarization information analysis ability in different application scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of polarization imaging. Specifically, it relates to an adaptive inversion method for arbitrary polarization channels with local sampling and alternating iteration. Background Art

[0002] In modern optical imaging and remote sensing technologies, polarization image acquisition and information analysis have become important means to improve imaging quality and target recognition capabilities. By detecting the polarization state of incident light waves, polarization imaging technology can provide richer multi-dimensional feature information of targets than traditional intensity imaging, and has broad application prospects in fields such as industrial inspection, biomedicine, and atmospheric monitoring. As the key to target information acquisition and feature deconstruction, a polarization imaging system usually obtains the light field information under multiple polarization channels (not less than three), that is, the images corresponding to different polarization angles (such as combinations of 0°, 60°, 120° or 0°, 45°, 90°, 135°, etc.), and then combines with the backend analysis algorithm to achieve the purpose of deconstructing target polarization features (such as degree of polarization, polarization angle, etc.).

[0003] At present, according to the working principle of the spectroscopic element and the acquisition method of the polarization image, the polarization imaging system can be divided into four categories: time-sharing type, amplitude-sharing type, aperture-sharing type and focal plane-sharing type. Among them, the amplitude-sharing type, aperture-sharing type and focal plane-sharing type polarization imaging systems usually rely on preset polarization channels and can only obtain polarization state information at three or four fixed directions and angles, which greatly limits the flexibility and adaptability of the system when applied in complex environments. In addition, for the aperture-sharing type and focal plane-sharing type imaging systems, the structural arrangement mode of the fixed channels will cause the loss of spatial resolution, and it is difficult to meet the precise reconstruction of the scene polarization characteristics. In contrast, the time-sharing polarization imaging system can arbitrarily obtain polarization channel images at different directions and angles by rotating and scanning the polarization optical device. It has the advantages of free tuning, flexible adaptation and high spatial resolution, and is expected to achieve more abundant polarization state information acquisition in the scene and target detection with the optimal polarization channel combination in complex and changing environments. Among them, as one of the core components of the time-sharing polarization imaging system, the positioning accuracy of the rotating scanning polarization filter module is the key to determining the measurement accuracy and reliability of the system. However, due to the influence of factors such as calibration error, mechanical vibration, assembly error and module performance, polarization imaging systems often have inevitable rotation angle errors during application, and rotation errors may cause the angles corresponding to the polarization channels to have the following two types of deviations: 1) Absolute angle error, that is, the actual absolute angle of a channel deviates from the theoretical value during the rotation of the polarization optical element; 2) Relative angle error, that is, the actual relative angle between two adjacent channels deviates from the theoretical value during the rotation of the polarization optical element. Regardless of the type of rotation error, this will cause the direction angles corresponding to the different polarization channels actually collected to deviate from the theoretical value, thereby reducing the reconstruction accuracy of the polarization characteristics. Therefore, how to quickly and accurately invert the actual corresponding angle position of the image information obtained by the polarization imaging system when the actual direction angle of the rotating scanning polarization filter module is unknown, so as to achieve more accurate polarization feature reconstruction has become a technical problem that needs to be solved urgently in the current research field.

[0004] In order to solve the problem of rotation angle error in the time-sharing polarization imaging system, Wibowo et al. ( IEEE Access 2019，7：28651 ) proposed a correction method by analyzing and studying the errors, and realized the calibration and compensation of the rotation error of the polarization imaging system by building a calibration device. Although this method has achieved good results in the laboratory, it relies on expensive and precise calibration equipment and complicated calibration procedures, and is difficult to apply to complex and changeable field environments. In recent years, Mu Yankui's research group at Xi'an Jiaotong University ( Opt. Lett. 2020, 45(1): 57Inspired by the phase-shifting interferometry technology, the polarization angle of the scene target and the analysis angle of the polarization optical device are calculated by least-squares iterative calculation between the polarization channel images until the iterative error converges. This is Method 1. Subsequently, Lu Xiang et al. IEEE Trans. Instrum. Meas. 2024, 73: 4505708 On this basis, a non-linear least-squares iterative method based on Levenberg-Marquart was proposed to achieve more accurate polarization channel angle extraction and polarization feature reconstruction. This is Method 2. Although the above methods have advantages such as high accuracy and convenient operation, in practical applications, due to the instability of the illumination environment and the influence of noise interference, there are the following two deficiencies: 1) There are problems of non-uniform unpolarized intensity values and polarized intensity values between different polarization channel images and between pixels in the same polarization channel image, seriously affecting the accuracy of angle inversion and feature reconstruction; 2) Selecting a suitable area for global inversion of the corresponding angles of the polarization channel images, that is, the strategy of "taking a part for the whole" can significantly reduce the computational amount, but the existing methods lack appropriate sampling strategy considerations, resulting in disadvantages such as long iteration time, large computational amount, and low inversion accuracy.

[0005] In summary, although the existing research has analyzed and studied the rotation angle error of the system, and the proposed iterative method can achieve polarization channel angle inversion when the rotation direction of the rotating scanning polarization filtering module is unknown, due to the consistency assumption conditions between channels and pixels and the iterative calculation of the pixel values included in the entire image, it lacks generality and robustness in practical applications and is difficult to meet the requirements of high-precision and high-efficiency polarization channel angle inversion and polarization feature reconstruction. Therefore, it is of great significance to propose an arbitrary polarization channel adaptive inversion method that can accurately invert the corresponding angles of the polarization channels efficiently under realistic situations such as environmental changes, noise interference, and uneven polarization feature distribution, so as to reduce the measurement error of the imaging system and improve the reconstruction accuracy of the polarization features. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to achieve accurate direction angle inversion and efficient reconstruction of polarization features, and improve the accuracy and robustness of polarization information analysis ability in different application scenarios. To overcome the defects of the above prior art (or related technologies), the present invention provides an arbitrary polarization channel adaptive inversion method with local sampling and alternating iteration.

[0007] The present invention provides an arbitrary polarization channel adaptive inversion method with local sampling and alternating iteration, including the following steps:

[0008] Step S1, for any scene target, use a polarization camera to collect multiple polarization channel images of the scene target at different direction angles;

[0009] Step S2: Estimate the global feature information based on each of the polarization channel images, perform clustering analysis using the K-Means method to obtain a clustering result image, and select a set of local sample points according to the clustering result image;

[0010] Step S3: Set an initial analysis angle according to the number of each of the polarization channel images;

[0011] Step S4: Based on the least squares method, perform matrix transformation on each of the polarization channel images according to the set of local sample points and the initial analysis angle to obtain a multi-polarization channel matrix, and solve based on the multi-polarization channel matrix to obtain an initial polarization angle matrix and an intermediate analysis angle matrix;

[0012] Step S5: Based on the least squares method, update the intermediate polarization angle matrix according to the multi-polarization channel matrix and the intermediate analysis angle matrix and solve to obtain an intermediate polarization angle;

[0013] Step S6: Determine whether the pre-set convergence condition is satisfied:

[0014] If yes, go to Step S7;

[0015] If no, alternately iterate between Step S4 and Step S5;

[0016] Step S7: Obtain a final analysis angle according to the final analysis angle matrix and the final polarization angle matrix after alternate iteration, obtain the Stokes vector of the incident light according to the final analysis angle matrix and the multi-polarization channel matrix, and reconstruct a polarization degree image according to the Stokes vector of the incident light.

[0017] Compared with the prior art, an arbitrary polarization channel adaptive inversion method with local sampling and alternate iteration of the present invention has the following advantages:

[0018] 1) In the present invention, it does not rely on additional hardware optimization and complex calibration procedures. By using the alternate iteration method, it can achieve accurate direction angle inversion and efficient reconstruction of polarization characteristics in the case where the direction angle of the polarization image is unknown and the initial analysis angle is randomly set. This method is simple and efficient, has strong applicability, and can handle various polarization image analysis scenarios;

[0019] 2) In the present invention, a local sampling strategy is adopted. It does not require global estimation of the polarization channel images. Instead, it starts from local samples with consistency similarity, performs inversion on local regions, and applies the results to the reconstruction of corresponding angles of the global image. This strategy makes the method have stronger robustness in complex scenarios and significantly improves the reconstruction accuracy and calculation efficiency;

[0020] 3) In the present invention, through the alternating iterative optimization calculation of the local area, compared with the traditional global optimization method, the calculation burden and resource consumption can be significantly reduced. In addition, this method is applicable to various types of polarization images and complex scene conditions, has strong adaptability, and can realize efficient and accurate polarization information inversion and reconstruction in a variety of practical applications, greatly improving the scalability and practical application value of the algorithm.

[0021] In a possible implementation manner, in the step S1, the polarization camera adopted integrates a rotatable and scanable polarization module, and by driving the polarization module to rotate to the positions of angles to obtain the polarization channel images with different direction angles.

[0022] In a possible implementation manner, the step S2 includes:

[0023] Step S21, based on each of the polarization channel images, combining the correlation between the polarization channels to obtain the maximum intensity image, the minimum intensity image, the estimated intensity image, and the estimated polarization degree image as the global feature information;

[0024] Step S22, combining according to the global feature information to obtain the feature vector of each pixel point in each of the polarization channel images, setting the number of clustering clusters and randomly generating a plurality of cluster centers, statistically calculating the minimum distance from each feature vector to each of the cluster centers, and using the K-Means method to perform clustering analysis to obtain the cluster labels of each pixel point to form the clustering result image;

[0025] Step S23, obtaining the sample points with the most significant polarization features and the optimal cluster labels to which the sample points belong according to the estimated polarization degree image;

[0026] Step S24, obtaining the in-cluster samples corresponding to the optimal cluster labels as the local sample point set.

[0027] In a possible implementation manner, in the step S21, the global feature information is obtained through the following calculation formula:

[0028] ;

[0029] Wherein,

[0030] represents the maximum intensity image;

[0031] represents the measured intensity value corresponding to the position of the mth pixel point at the angle of ;

[0032] represents the minimum intensity image;

[0033] represents the estimated intensity image;

[0034] represents the estimated degree of polarization image.

[0035] In a possible implementation manner, the step S4 includes:

[0036] Step S41, obtaining the corresponding multi-polarization channel matrix according to the local sample point set and the initial analysis angle;

[0037] Step S42, based on the least squares method, performing matrix transformation on the multi-polarization channel matrix and the initial analysis angle matrix corresponding to the initial analysis angle to obtain the initial polarization angle matrix;

[0038] Step S43, based on the least squares method, performing matrix transformation on the multi-polarization channel matrix and the intermediate polarization angle matrix after the change of the initial polarization angle matrix to obtain the intermediate analysis angle matrix.

[0039] In a possible implementation manner, in the step S41, the multi-polarization channel matrix is obtained through the following calculation formula:

[0040] ;

[0041] wherein,

[0042] represents the multi-polarization channel matrix;

[0043] represents the intensity vector of the polarization channel image;

[0044] represents the cosine vector of the polarization angle corresponding to the polarization channel image;

[0045] represents the sine vector of the polarization angle corresponding to the polarization channel image;

[0046] represents the cosine vector of the initial analysis angle;

[0047] represents the sine vector of the initial analysis angle.

[0048] In a possible implementation manner, the convergence condition in the step S6 is:

[0049] ;

[0050] wherein,

[0051] Denote the intermediate polarization angle of the k-th iteration;

[0052] Denote the intermediate polarization angle of the (k-1)-th iteration;

[0053] Denote the preset convergence accuracy.

[0054] In a possible implementation, the step S7 includes:

[0055] Step S71, obtain the final analysis angle according to the final analysis angle matrix and the final polarization angle matrix after alternating iteration;

[0056] Step S72, based on the least squares method, perform matrix transformation according to the multi-polarization channel matrix and the final analysis angle matrix to obtain the Stokes vector of the incident light;

[0057] Step S73, reconstruct the polarization degree image according to the Stokes vector.

[0058] In a possible implementation, in the step S71, the final analysis angle is obtained through the following calculation formula:

[0059] ;

[0060] Wherein,

[0061] Denote the final analysis angle;

[0062] Denote the final polarization angle matrix;

[0063] Denote the final analysis angle matrix;

[0064] Denote the second column element of the final analysis angle matrix;

[0065] Denote the third column element of the final analysis angle matrix.

[0066] In a possible implementation, in the step S73, the polarization degree image is obtained through the following calculation formula:

[0067] ;

[0068] ;

[0069] Wherein,

[0070] represents the degree of polarization image;

[0071] represents the Stokes vector of the incident light;

[0072] represents the first parameter of the Stokes vector;

[0073] represents the second parameter of the Stokes vector;

[0074] represents the third parameter of the Stokes vector;

[0075] represents the multi-polarization channel matrix;

[0076] represents the final analysis angle matrix. Brief Description of the Drawings

[0077] Figure 1 is the flowchart of the steps of the present invention;

[0078] Figure 2 is the schematic diagram of the image acquisition process of the present invention;

[0079] Figure 3 is the schematic diagram of the polarization channel images at different direction angles of the present invention;

[0080] Figure 4 is the schematic diagram of the global feature image of the present invention;

[0081] Figure 5 is the schematic diagram of the clustering result image of the present invention;

[0082] Figure 6 is the schematic diagram of the local sample point set mapping image and the Malus fitting curve of the present invention;

[0083] Figure 7 is the schematic diagram of the reconstructed Stokes parameter image and the degree of polarization image of the present invention;

[0084] Figure 8 is the schematic diagram of the residual image of reconstructing the polarization characteristics by different methods of the present invention. Detailed Embodiments

[0085] First of all, those skilled in the art should understand that these embodiments are only used to explain the technical principles of the embodiments of the present invention, and are not intended to limit the protection scope of the embodiments of the present invention. Those skilled in the art can adjust them according to needs to adapt to specific application scenarios.

[0086] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0087] Referring to Figure 1 , an arbitrary polarization channel adaptive inversion method based on local sampling and alternating iteration is disclosed in an embodiment of the present invention, including:

[0088] Step S1, for any scene target, a plurality of polarization channel images including the scene target with different direction angles are collected by using a polarization camera;

[0089] Step S2, global feature information is estimated according to each of the polarization channel images, and clustering analysis is performed by using the K-Means method to obtain a clustering result image, and a local sample point set is selected according to the clustering result image;

[0090] Step S3, an initial analysis angle is set according to the number of each of the polarization channel images;

[0091] Step S4, based on the least square method, matrix transformation is performed on each of the polarization channel images according to the local sample point set and the initial analysis angle to obtain a multi-polarization channel matrix, and an initial polarization angle matrix and an intermediate analysis angle matrix are solved based on the multi-polarization channel matrix;

[0092] Step S5, based on the least square method, the intermediate polarization angle matrix is updated according to the multi-polarization channel matrix and the intermediate analysis angle matrix, and an intermediate polarization angle is solved;

[0093] Step S6, determining whether a pre-set convergence condition is satisfied:

[0094] If so, go to step S7;

[0095] If not, perform alternating iteration on step S4 and step S5;

[0096] Step S7, a final analysis angle is obtained according to the final analysis angle matrix and the final polarization angle matrix after alternating iteration, a Stokes vector of incident light is obtained according to the final analysis angle matrix and the multi-polarization channel matrix, and a polarization degree image is reconstructed according to the Stokes vector of the incident light.

[0097] Referring to Figure 2, in the embodiments of the present invention, experiments are conducted on a submarine model to construct an experimental dataset and provide an example. Among them, the imaging system mentioned in the present invention is a polarization imaging camera integrated with a rotating scanning polarization filtering module, which can collect 180 polarization channel images with a minimum interval of 1° in the range of 0° to 180°; the imaging detector is a CCD black-and-white industrial camera with an imaging resolution of 960×1280 and a spectral response range of 380~1050nm; during the process of collecting multi-polarization channel images, indoor ambient light illumination is used, and both the submarine model and the camera remain stationary; in order to suppress the influence of random noise on the image quality and establish a reliable real dataset, 50 images are collected in the same polarization channel direction under the same scenario and averaged.

[0098] In step S1, 4 polarization channel images containing scene targets are collected using a polarization camera. The polarization camera is a time-sharing system integrated with a rotating scanning polarization module, which drives the polarization optical device to rotate to , , and angle positions (in this embodiment, a ±5° angle deviation is introduced on the basis of the traditional 0° / 45° / 90° / 135° as the unknown rotation angle situation during the actual working process; for any other angle deviation, the method proposed in the present invention is applicable). The 4 polarization channel images obtained in different direction angles are , , and .

[0099] See Figure 3 , which are the 4 polarization channel images obtained in different direction angles in the embodiments of the present invention. It can be seen that as the direction angle of the polarization optical element changes, the light intensity in the local area of the submarine model shows a trend of changing from bright to dark, which indicates that the polarization imaging process has a modulating effect on the reflected light intensity, and the area with obvious light intensity change has more significant polarization characteristics.

[0100] To further illustrate the principle of the method of the present invention, in combination with the Stokes-Muller matrix method, it is assumed in step S1 that the Stokes vector can represent the polarization state of light. For a polarization imaging optical system, represents the Stokes vector of the incident light, represents the Stokes vector of the outgoing light. When the light beam passes through the imaging system, the modulation effect of the system on the light can be represented by the Mueller matrix, that is:

[0101] ;

[0102] According to the Mueller matrix of the ideal linear polarization device, the above equation can be expressed as:

[0103] ;

[0104] In the linear polarization imaging system, the photosensitive pixel unit of the imaging detector is only sensitive to the intensity of the incident light. Therefore, obtaining N the light intensities of the corresponding pixels in the polarization channel images with different direction angles can be expressed as:

[0105] ;

[0106] where n represents the polarization channel sequence, and is the total number of polarization channels collected; m represents the pixel sequence in the image, ; represents the measured intensity value corresponding to the m-th pixel position when the polarization direction is angle, ;

[0107] Assume that the degree of polarization corresponding to the m-th pixel of the scene target in the detector is , and the polarization angle is . Then the Stokes vector of the incident light can be expressed as:

[0108] ;

[0109] where, in the process of linear polarization imaging, the circular polarization parameter is ignored, that is ;

[0110] Therefore, combining the above equation, the polarization channel images with different direction angles obtained can also be expressed as:

[0111] ;

[0112] After simplification, it can be obtained:

[0113] .

[0114] Furthermore, step S2 is disassembled and described step by step. Step S2 includes:

[0115] Step S21, estimating the global feature information according to any polarization channel image;

[0116] In step S1, the multi-polarization channel image sequence set is obtained Based on this, combined with the correlation between polarization channels, global feature information is estimated, specifically including: the maximum intensity image , the minimum intensity image , the estimated intensity image and the estimated degree of polarization image , where and can be estimated by traversing the pixel values contained in the image, that is:

[0117] ;

[0118] ;

[0119] The degree of polarization (DoP) is an index used to describe the degree of light polarization and can be calculated from the maximum intensity image and the minimum intensity image . Therefore, the estimated degree of polarization image can be expressed as:

[0120] ;

[0121] where ranges from [0, 1], 1 represents completely polarized light, and 0 represents unpolarized light;

[0122] The estimated intensity image can be approximated by the average intensity value of the multi-polarization channel polarization image, that is, by calculating the average value of all polarization channels, that is:

[0123] ;

[0124] According to the above steps, the maximum intensity image , the minimum intensity image , the estimated intensity image and the estimated degree of polarization image are shown in Figure 4 from left to right in sequence. It can be seen that due to the inherent polarization properties of the material itself and the light field interaction between the interface and the environment, these four images can fully display the differential feature information in different regions, which provides a reliable data basis for subsequent global feature analysis and regional sampling;

[0125] Step S22: Statistically analyze the global feature information and set the number of clustering clusters for clustering analysis;

[0126] Based on the global feature images estimated in step S21, they are combined into a feature vector for each pixel point, that is:

[0127] ;

[0128] Among them, F is m a 4×4 matrix;

[0129] In order to calculate the sum of the squares of the distances from each eigenvector in F to its cluster center, the K-Means method is used for clustering analysis. According to the set number of clustering clusters , randomly generate cluster centers , where the minimized distance from each eigenvector to the cluster center can be expressed as:

[0130] ;

[0131] Among them, represents the indicator function. If belongs to cluster q, then ; otherwise ;

[0132] After the clustering analysis is completed using the K-Means method, the cluster label of pixel point m can be obtained, and a clustering result image is formed:

[0133] ;

[0134] This clustering result image represents the final classification of the clustering analysis of the global image samples based on the global feature information;

[0135] S23: Search for the sample points with the most significant polarization features and obtain the corresponding optimal cluster numbers;

[0136] Based on the estimated polarization degree image obtained in step S21, the sample points with the most significant polarization features and their coordinate positions can be expressed as:

[0137] ;

[0138] Therefore, according to the clustering result image in step S22, the optimal cluster label to which the sample point with the most significant polarization features (coordinate position: x = 6; y = 678) belongs is , as shown in Figure 5 ;

[0139] S24: Obtain the local sample point set according to the optimal in-cluster sample distribution;

[0140] In the clustering result image of step S22 and the optimal cluster label in step S23 is Based on this, the in-cluster samples corresponding to the optimal cluster label, that is, the set of obtained local sample points is:

[0141] ;

[0142] Among them, S = 713 is the total number of local sample points, and .

[0143] According to the content in step S22, in the clustering result image of the embodiment of the present invention, the polarization characteristics are related to the material surface properties, the incident light angle, and the included angle with the surface normal; the sample points in different regions of the global image can also be clustered into the same cluster; the sample points in the local region can also exhibit different polarization characteristics due to the influence of the target surface curvature, etc. Therefore, the local sampling method proposed by the present invention can effectively search for sample points with similar features globally, which provides a basis for subsequent angle inversion and feature reconstruction.

[0144] According to the content in step S24, the mapped image of the local sample point set and the Malus fitting curve in the embodiment of the present invention are shown in Figure 6 as shown. The red area is the obtained local sample points, and the five-pointed stars of different colors represent the analysis points selected in each area. It can be seen that the sampled local data has similar polarization degree characteristics, and the light intensity distribution curves are in good agreement.

[0145] Furthermore, the initial analysis angle in any direction preset in step S3 is , that is: , among which, , N = 4 is the number of polarization channels.

[0146] Furthermore, in step S4, combining the local sample points obtained in step S24 and the initial analysis angle preset in step S3, in step S41, the expression of the channel polarization image can be rewritten by using the matrix method as:

[0147] ;

[0148] Among them, the matrix I represents the S × N multi-polarization channel matrix corresponding to the local samples obtained in step S24; represents the intensity vector of the polarization channel image, ; represents the cosine vector of the polarization angle corresponding to the polarization channel image, ; represents the sine vector of the polarization angle corresponding to the polarization channel image, ; represents the unit vector; Represents the cosine vector of the preset initial analysis angle, ; Represents the sine vector of the preset initial analysis angle, ;

[0149] Step S42, considering that the multi-polarization channel matrix I and the initial analysis angle matrix are known quantities, perform matrix transformation through the least squares principle to solve the initial polarization angle matrix , that is:

[0150] ;

[0151] Step S43, according to the known multi-polarization channel matrix I and the intermediate polarization angle matrix ( is the initial polarization angle matrix), perform matrix transformation through the least squares principle to solve the intermediate analysis angle matrix , that is:

[0152] ;

[0153] Among them, k represents the number of alternating iterations, k = 0, 1, 2,... K .

[0154] Furthermore, step S5 is disassembled and described step by step. Step S5 includes:

[0155] S51: Update the intermediate polarization angle matrix using the least squares method;

[0156] According to the known multi-polarization channel matrix I and the intermediate analysis angle matrix , perform matrix transformation through the least squares principle to update the intermediate polarization angle matrix , that is:

[0157] ;

[0158] S52: Calculate the intermediate polarization angle;

[0159] According to the intermediate polarization angle matrix solved in step S51 and the polarization angle definition, the intermediate polarization angle can be expressed as:

[0160] ;

[0161] Among them, and respectively represent the second and third column elements of the initial polarization angle matrix ;

[0162] S53: Calculate the intermediate polarization angle matrix;

[0163] Considering that updating the polarization angle matrix V can introduce perturbations that conform to the characteristics of the desired solution, achieve better and faster analytical convergence, and avoid falling into an infinite loop. Therefore, combining the intermediate polarization angle obtained in step S52 and the definition of the polarization angle matrix, the calculated intermediate polarization angle matrix can be expressed as:

[0164] .

[0165] Furthermore, in step S6, when the convergence condition is satisfied, the cross-iteration stops. The convergence condition is:

[0166] ;

[0167] where represents the convergence accuracy, is the intermediate polarization angle calculated in the k th iteration, is the intermediate polarization angle calculated in the k -1th iteration.

[0168] Furthermore, step S7 is disassembled and described step by step. Step S7 includes:

[0169] S71: Calculate the final analysis angle;

[0170] According to the intermediate analysis angle matrix solved in steps S5 - S6 and the definition of the analysis angle, the final analysis angle can be expressed as:

[0171] ;

[0172] where and represent the second and third column elements of the final analysis angle matrix respectively. The final analysis angle solved in this embodiment is: .

[0173] S72: Calculate the Stokes parameter image;

[0174] Combining the final analysis angle obtained in step S71 and the definition of the analysis angle matrix, the final analysis angle matrix can be expressed as:

[0175] ;

[0176] According to the known multi-polarization channel matrix I and the final analysis angle matrix , through the principle of least squares for matrix transformation, the Stokes vector of the incident light is calculated , namely:

[0177] ;

[0178] S73: Calculate the degree of polarization image;

[0179] According to the Stokes vector obtained in S72 , the degree of polarization image can be reconstructed as follows:

[0180] ;

[0181] According to the above steps, the Stokes parameters image, Stokes parameter , image Stokes parameter image and degree of polarization image are arranged from left to right as shown in Figure 7 .

[0182] To verify the effectiveness and advancement of the technical solution of the present invention, two methods described in the background art are used for comparison, and different polarization channel angle inversions are performed for the scenarios referred to in Figure 1 , and the results are shown in Table 1 below:

[0183] Table 1 Comparison of polarization channel inversion angle results of different methods

[0184] ;

[0185] Among them, due to the complexity of the test atlas, the two methods in the background art got stuck in local minima during the test, resulting in the inability to effectively converge the iterative error; the analysis angles inverted by Method 1 and Method 2 were obtained by solving based on the local sampling method proposed in this method. Under the condition of arbitrarily selecting the analysis angle, the average error between the analysis angle inverted by the present invention and the true analysis angle is only 0.64°. Due to the complexity of the test scenario, it is difficult for Method 1 and Method 2 to realize the iterative inversion of the analysis angle, and the average errors are 2.61° and 6.34° respectively based on the local sampling method proposed in the present invention.

[0186] In addition, in order to more intuitively compare the quality of the polarization feature images reconstructed by different methods, in this embodiment, the difference between the reconstructed image and the true image is calculated to further compare the deviation occurring during the reconstruction process. Refer to Figure 8 as the residual image of the polarization features reconstructed by different methods. It can be seen that the residual corresponding to the Stokes parameter image and the degree of polarization image reconstructed by the method proposed in the present invention is the smallest, followed by Method 1, and Method 2 is the worst.

[0187] Finally, indicators such as Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Gradient Magnitude Similarity Deviation (GMSD) are used for quantitative evaluation of image quality. Among them, RMSE is used to evaluate the error between the reference image and the reconstructed image. The smaller the RMSE value, the more similar the two images are, and the higher the quality of the reconstructed image. PSNR is an indicator to measure the global difference between the reconstructed image and the reference image. The larger the PSNR value, the more similar the two images are, and the higher the quality of the reconstructed image. GMSD evaluates the difference between images based on the gradient information of the images. The smaller the GMSD value, the more similar the two images are. See Table 2 below:

[0188] Table 2 Comparison of evaluation index results for reconstructed polarization feature images by different methods

[0189] ;

[0190] The evaluation index results of reconstructed polarization feature images by different methods Figure 8 are consistent with the visualization results, that is, the method proposed in the present invention is superior to the existing methods in both the reconstructed Stokes parameter image and the degree of polarization image, which further verifies the superiority of the technical solution of the present invention.

[0191] In the description of the present invention, the descriptions of reference terms such as "one embodiment", "some embodiments", "in this embodiment", "specific examples", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without conflict, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0192] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. An adaptive inversion method for any polarization channel with local sampling and alternating iteration, characterized in that It includes the following steps: Step S1: For any scene target, use a polarization camera to collect multiple polarization channel images of different directional angles containing the scene target; Step S2: Estimate the global feature information based on each of the polarization channel images, perform clustering analysis using the K-Means method to obtain a clustering result image, and select a local sample point set according to the clustering result image; Step S3: Set an initial analysis angle according to the number of each of the polarization channel images; Step S4: Based on the least squares method, perform matrix transformation on each of the polarization channel images according to the local sample point set and the initial analysis angle to obtain a multi-polarization channel matrix, and solve based on the multi-polarization channel matrix to obtain an initial polarization angle matrix and an intermediate analysis angle matrix; Step S5: Based on the least squares method, update the intermediate polarization angle matrix according to the multi-polarization channel matrix and the intermediate analysis angle matrix and solve to obtain an intermediate polarization angle; Step S6: Determine whether the pre-set convergence condition is satisfied: If yes, go to Step S7; If not, perform alternating iteration on Step S4 and Step S5; Step S7: Obtain a final analysis angle according to the final analysis angle matrix and the final polarization angle matrix after alternating iteration, obtain the Stokes vector of the incident light according to the final analysis angle matrix and the multi-polarization channel matrix, and reconstruct a polarization degree image according to the Stokes vector of the incident light.

2. The adaptive inversion method for any polarization channel with local sampling and alternating iteration according to claim 1, wherein In the step S1, the polarization camera adopted integrates a rotatable and scanable polarization module, and by driving the polarization module to rotate to the positions of the angles to obtain the polarization channel images with different angular directions.

3. The adaptive inversion method for any polarization channel with local sampling and alternating iteration according to claim 1, characterized in that Step S2 includes: Step S21: Based on each of the polarization channel images, combine the correlation between the polarization channels to obtain a maximum intensity image, a minimum intensity image, an estimated intensity image, and an estimated polarization degree image as the global feature information; Step S22: Combine the global feature information to obtain a feature vector of each pixel point in each of the polarization channel images, set the number of clustering clusters to randomly generate multiple cluster centers, count the minimum distance of each feature vector to each cluster center, and perform clustering analysis using the K-Means method to obtain the cluster labels of each pixel point to form the clustering result image; Step S23: Obtain the sample points with the most significant polarization characteristics and the optimal cluster label to which the sample points belong according to the estimated polarization degree image; Step S24: Obtain the in-cluster samples corresponding to the optimal cluster label as the local sample point set.

4. The adaptive inversion method for any polarization channel with local sampling and alternating iteration according to claim 3, wherein In Step S21, the global feature information is obtained through the following calculation formula: ； where representing the maximum intensity image; Indicates the measured intensity value corresponding to the position of the m-th pixel point at an angle ; represents the minimum intensity image; represent the predicted strength image; Indicates the predicted degree of polarization image.

5. The adaptive inversion method for any polarization channel with local sampling and alternating iteration according to claim 1, characterized in that Step S4 includes: Step S41: Obtain the corresponding multi-polarization channel matrix according to the local sample point set and the initial analysis angle; Step S42: Based on the least squares method, perform matrix transformation on the multi-polarization channel matrix and the initial analysis angle matrix corresponding to the initial analysis angle to obtain the initial polarization angle matrix; Step S43: Based on the least squares method, perform matrix transformation on the multi-polarization channel matrix and the intermediate polarization angle matrix after the change of the initial polarization angle matrix to obtain the intermediate analysis angle matrix.

6. The adaptive inversion method for any polarization channel with local sampling and alternating iteration according to claim 5, characterized in that In Step S41, the multi-polarization channel matrix is obtained through the following calculation formula: ； where representing the multi-polarization channel matrix; represent the intensity vector of the polarization channel image; Represents the cosine vector of the polarization angle corresponding to the polarization channel image; Indicates the sine vector of the polarization angle corresponding to the polarization channel image; Indicates the cosine vector of the initial analysis angle; Represents the sine vector of the said initial analysis angle.

7. The local sampling and alternating iteration-based arbitrary polarization channel adaptive inversion method according to claim 1, wherein The convergence condition in Step S6 is: ； where representing the intermediate polarization angle of the k-th iteration; representing the intermediate polarization angle of the (k - 1)-th iteration; Indicates a preset convergence accuracy.

8. The adaptive inversion method for any polarization channel with local sampling and alternating iteration according to claim 1, characterized in that The said step S7 includes: Step S71, obtaining the final analysis angle according to the final analysis angle matrix and the final polarization angle matrix after alternating iteration; Step S72, based on the least squares method, performing matrix transformation according to the multi-polarization channel matrix and the final analysis angle matrix to obtain the Stokes vector of the incident light; Step S73, reconstructing the polarization degree image according to the Stokes vector.

9. The local sampling and alternating iteration-based arbitrary polarization channel adaptive inversion method according to claim 8, characterized in that In the said step S71, the final analysis angle is obtained through the following calculation formula: ； where, representing the said final analysis angle; representing the final polarization angle matrix; represents the final analysis angle matrix; represent the elements of the second column of the final analysis angle matrix; Represents the elements of the third column of the final analysis angle matrix.

10. The local sampling and alternating iteration-based arbitrary polarization channel adaptive inversion method according to claim 8, wherein In the said step S73, the polarization degree image is obtained through the following calculation formula: ； ； where, representing the degree of polarization image; The Stokes vector representing the incident light; represents a first parameter of the Stokes vector; Represents the second parameter of the Stokes vector; represents the third parameter of the Stokes vector; represents the multi-polarization channel matrix; Indicates the final analysis angle matrix.

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