## 3 Results
### Real Image Testing  
We established a focal plane polarization detection system operating in the short-wave infrared range to collect real images to test our algorithm. The size of the focal plane images was \(320\times 256\) pixels. The obtained test results are shown in Figure 5.  
The selected test scene had cloudy weather conditions. The drone used as the target was the DJI Spirit 4 Pro (DJI, Shenzhen, China). All display results were normalized to the same quantization level. No additional image enhancement algorithms were applied to the presentation results. Figure 5 shows the full-field S0 image, revealing a dim target. Among the various algorithms tested, the EARI algorithm produced the fuzziest S0 image. In several other channels, it was not possible to identify the target as a drone. However, some areas of the \(DP\) image allowed for the calculation of the degree of polarization. The NP and PCDP algorithms did not clearly show the target in the S1 image. In the \(DP\) image, neither of these two algorithms accurately calculated the polarization degree of the UAV. Man-made objects, being more polarized than the natural background, did not exhibit this phenomenon well in the results. These two algorithms introduced significant errors in the \(AP\) images. Due to the drone's orientation towards the lens, the polarization angles of its left and right sides did not significantly differ. As for MPPOCS, it can be observed that the \(S0\) image was overly blurry. The filter utilized in this algorithm reduced noise in the entire image, but it also sacrificed some high-frequency information. The \(S1\) and \(S2\) images performed better than with the NP and PCDP algorithms. The \(DP\) image provided a clearer representation of the target's polarization information compared to the EARI algorithm. The \(AP\) image was slightly more distinct than with the first three algorithms. Figure 4: The objective evaluation indexes of our algorithm and MPPOCS for a tower, as measured under varying noise conditions. (**a**) Objective evaluation indexes of S0. (**b**) Objective evaluation indexes of DP. Ultimately, our proposed \(S0\) algorithm yielded the best results for the image. The brackets on the left and right holding the rotors were separated from the drone's fuselage. The \(S1\) and \(S2\) images outperformed other algorithms, particularly in the \(AP\) image, where our proposed algorithm accurately reconstructed the entire shape of the UAV. With a certain level of prior knowledge, the UAV could be identified as the DJI Genie 4 Pro drone even without specialized equipment. Additionally, in the \(DP\) image, our reconstruction results displayed the target's polarization degree. The target's edges were sharp, and the difference between the target and background was highly noticeable, providing a strong foundation for the recognition of small polarized targets. Finally, the processing time of the proposed algorithm was statistically analyzed and is shown in Table 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **EARI** & **NP** & **PCDP** & **MPPOCS** & **This Work** \\ \hline Running Time/s & 0.31 & 0.19 & 0.03 & \(1.18\) & \(18.84\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The real image reconstruction results of each algorithm’s running time. Figure 5: Real scene reconstruction results of each algorithm. Our algorithm achieved good performance in image processing. In the S0 image, the body of the unmanned aerial vehicle is distinguished from the fixed rotor support structures on its sides. Our algorithm effectively restored the form of the unmanned aerial vehicle. Even without specialized equipment, we were capable of visually recognizing the target. In the AP and DP images, our reconstruction results distinctly demonstrate the polarization degree of the target. The target’s edges are clearly visible, and there is a noticeable contrast between the target and background. Our algorithm provides a reliable foundation for identifying small polarized targets.