## 3 Algorithm Flow
### Polarization Image and Spectral Decoupling and Spectral Reconstruction
#### 3.1.2 Polarization Interference Image Registration and Polarization Spectral Reconstruction  
For the segmented image units, the interference order is determined by the phase space modulation mode of the system interference core device, and the segmented image units are matched according to the optical path difference. However, the polarization interference data cube set obtained by the optical path difference matching cannot accurately locate the image units, and there are large matching errors, which will affect the accuracy of spectral reconstruction. In order to accurately obtain the polarization interference intensity sequence of any point in the target scene to achieve high-precision spectral reconstruction, it is necessary to register the image units after the optical path difference matching. Therefore,  
Figure 4: Flowchart of decoupling and reconstruction of polarization spectral data cube. this paper proposes an image registration algorithm based on SUSAN corner detection combined with SIFT feature matching. The SIFT algorithm is a feature-matching algorithm based on scale space and using neighborhood gradient information to extract and describe features. SIFT features have good robustness to image rotation, scaling, and noise, but the feature point extraction is unstable, and most of the feature points are not corner points, which cannot well reflect the structural characteristics of the image. SUSAN corner detection is a feature point acquisition method based on grayscale level. It can detect the edge and corner of the image, and has the advantages of strong anti-noise ability, high precision, simpleness, and effectiveness. SUSAN corner detection is used to obtain the corner information of the image unit, and the SIFT descriptor is used to describe the feature point in the feature point description stage, so as to obtain stable and accurate feature point information. The specific steps are as follows. 1. Perform SUSAN corner detection for each image unit to obtain its corner information set (2) Generate SIFT feature descriptors according to the corner information set obtained in step (1);
2. Perform SIFT feature coarse matching and RANSAC feature fine matching for each image unit to obtain the feature point information set;
3. Take the intersection of the feature points obtained in step (1) and step (3) to obtain the final feature point set and complete image registration. SUSAN corner detection is essentially a detection method based on a window template [21]. It uses a circular window of fixed size, directly compares the grayscale difference between the pixels in the circular window and the central pixel, counts the grayscale difference in the whole circular window, and uses the threshold judgment to detect corners. Since the process of SUSAN corner detection involves no image pixel gradient, it has good robustness in the presence of noise. Compared with the detection method based on gradient, situations where the central neighborhood and the central area angular response are difficult to distinguish are prevented, therefore a simple selection of local maximum values could lead to the non-maximum suppression. After SUSAN corner detection, a preliminary corner information set is obtained, and then the SIFT feature matching algorithm is used to complete the corner information set and image registration [22]. The gradient magnitude and gradient amplitude of image pixels are obtained in the region with a radius of \(3\times 1.5\)\(\sigma\) (\(\sigma\) as the scale) based on the feature points through SUSAN Corner Detection, and then the gradient histogram is constructed, and the peak direction of the histogram becomes the main direction of the feature points. The region around the corner is rotated clockwise to the main direction angle, and a rectangular window of \(16\times 16\) with the corner as the center is selected in the rotated region, and then it is divided into \(4\times 4\) sub-regions.