## 3 Results  
To validate the proposed method, we created a prototype near-real-time short-wave infrared time-sharing polarization system for collecting training and simulation data, building upon our previous work. As shown in Figure 2. The system utilized a custom short-wave detector that operated within the 0.95-1.65 \(\upmu\)m wavelength range. Additionally, a self-designed zoom lens with a resolution of 120 lp/mm was employed. Real-time communication with the host optical signal was facilitated by the fully electronic design of the rotating target wheel component. The system offered a polarization time resolution of 25 frames. A total of 60 scenes, encompassing indoor and outdoor settings, were captured. Amongst these scenes, 54 were used to form the training dictionary set, while 6 scenes were reserved for simulation testing. The 50 images from the simulation test are depicted in Figure 1 in Appendix A, showcasing cars, a window, a wall, a building, a tower, and a tank. Each image has a resolution of 640 \(\times\) 512 pixels.  
In this paper, four interpolation methods were utilized: the Newton polynomial-based interpolation method (NP) [14], the Edge Sensing Residual Interpolation method (EARI) [15], a polarization difference priority-based interpolation algorithm (PCDP) [16], and the Micro-scanning Polarization Projection On Convex Sets (MPPOCS) algorithm [19], which we previously proposed. As the MPPOCS algorithm is a multi-frame super-resolution algorithm, we modified its input. In the simulation experiment, we simulated the micro-scan image input using a single frame image. For the real image test, we directly employed a multi-frame image as the input.  
In terms of objective evaluation indicators, three commonly used methods in super-resolution reconstruction tasks were selected: a structural similarity coefficient (SSIM), the Peak Signal-to-Noise Ratio (PSNR), and Root Mean Square Error (RMSE). These evaluation  
Figure 2: Production of the near-real-time short-wave infrared time-sharing polarization system. In our study, the incident natural light is partially polarized when it reflects off the target. Subsequently, it passes through a polarizer to obtain linear polarization. The linearly polarized light then travels through the lens and reaches the shortwave infrared detector. In terms of the physical products we manufactured, the foremost component was a motor-controlled rotating target wheel. We achieved high-precision angle control using a grating ruler. The entire system was connected to the upper computer via fiber optics for transmission.  
criteria were applied to the normalized images of \(S0\), \(S1\), \(S2\), \(DP\), and \(AP\), respectively. A higher SSIM indicated that the reconstructed image closely resembled the reference image in terms of intensity, contrast, and structure. The accuracy of each polarization component's reconstructed image reflected the true polarization characteristics of the object. The noise in the polarization image mainly manifested as false polarization. PSNR, which measures the ratio of peak signal energy to average noise energy, was used to assess the pseudo-polarization level in each component of the reconstructed polarization image. RMSE was used to gauge the degree of similarity between the reconstructed polarization image and the true polarization image in terms of pixel position. A smaller RMSE value indicated a closer match between the reconstructed and true polarization values for the same pixel position. By combining these three objective evaluation measures with subjective criteria, a comprehensive analysis of the proposed reconstruction method was conducted.