## 3 Results
### Noise Robustness  
Limited by the manufacturing process of the detector, the noise of a focal plane polarization system is generally higher than that of a time-sharing polarization system. Therefore, it was crucial to analyze the noise robustness of the algorithm. Based on the comparison results presented in the previous section, both the proposed method and MPPOCS outperformed other methods. Consequently, this section compares the noise robustness between these two methods. The test image again was a tower. Gaussian white noise with mean 0 and standard deviation ranging from 0 to 3 was added to the focal plane image synthesized by each bias detection component of the tower. The value of \(\lambda\), introduced in this paper, was set to 0.02. The objective indexes for the reconstructed \(S0\) and \(DP\) images of the two algorithms are depicted in Figure 4, while the reconstructed indexes for the \(S1\), \(S2\), and \(AP\) images are shown in the attached Figures A2 and A3a,b. SSIM is represented on the black axis, PSNR on the intermediate axis, and RMSE on the blue axis. The + line represents the algorithm proposed in this paper, and the \(\times\) line represents MPPOCS.  
In Figure 4a, the objective evaluation index of the proposed algorithm gradually decreased as the level of noise increased, with a decline rate higher than that of MPPOCS. In Figure 4b, when the mean noise was 0.9, the SSIM index of our proposed method started to become lower than that of MPPOCS. When the mean noise values were 2.7 and 2.8, respectively, both PSNR and RMSE were weaker than those of the MPPOCS algorithm. Similar results were observed for the \(S1\), \(S2\), and \(AP\) images. In this set of experiments, we kept the \(\lambda\) values unchanged. These findings proved that the proposed algorithm possessed inherent noise resistance, as the reconstruction results were superior to MPPOCS when the noise level was low. Additionally, our algorithm demonstrated better performance in the case of low noise, as shown in Figure 4a. This can be attributed to the fact that the \(S0\) image was less sensitive to the noise in each channel compared to the other components, resulting in consistently optimal reconstruction results. We analyzed the results to ascertain the reasons behind the MPPOCS algorithm's high noise robustness. The inclusion of wavelet threshold noise reduction in the algorithm grants it a natural adaptability to noise, especially in the presence of high levels of noise. However, in the context of weak noise, this can lead to excessively smooth image results, as illustrated in Figure 3.  
Figure 3: The simulation reconstruction results of each algorithm for a tower. The ground truth image provides a high-resolution representation of each image. The EARI algorithm exhibits a notable false image.  
bias in the AP and DP images, and the S0 image appears blurred. Additionally, the edge details show a noticeable jagged appearance. Both the NP and PCDP algorithms display clear high-frequency errors in the reconstructed S1 and S2 images, as they rely on a set of orthogonal polarization angle image differences to obtain these components. When compared with the ground truth's S1 and S2 images, it is evident that these two methods yield larger errors. Although visually improved, the DP and AP images reconstructed by the NP and PCDP algorithms display sharp edges resulting from pseudo-polarization effects. Due to the strong noise reduction algorithm used in the MPPOCS algorithm, some loss of detail is observed, leading to a relatively fuzzy reconstruction of the S0 image. Our proposed algorithm achieves a close resemblance to the ground truth in terms of the S1 and S2 images; however, it also captures additional details in the AP and DP images compared to the MPPOCS algorithm.