## 4 PSIFTIS Imaging Experiment Results and Data Processing
### Infrared Polarization Image Fusion
(2) above, the fusion rules of taking the maximum value of regional contrast, the average value of the corresponding pixels, and the minimum value regional energy are selected to compare with the algorithm in this paper. The fusion effects of high-frequency vertical, horizontal, and diagonal components are shown in Figures 18-20 and the objective index analysis data are shown in Tables 3-5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **AG** & **PSNR** & **EN** & **MI** \\ \hline PI & 7.2223 & 4.5937 & 6.4100 & / \\ PF & 10.0424 & 10.5546 & 6.4107 & / \\ Maximum Value of & 8.2208 & 4.6046 & 6.4335 & 8.3545 \\ Regional Energy & 11.7738 & 10.5546 & 6.4766 & 8.3139 \\ Maximum Value of & 11.7746 & 10.3954 & 6.4806 & 8.3181 \\ Regional Energy & & & & \\ RCD-AG-GS & 13.1196 & 57.5921 & 6.4772 & 12.9612 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation and analysis of different low-frequency component fusion rules. Figure 17: Comparison of different low-frequency component fusion rules: (a) the region energy takes a large; (b) the region contrast takes a large; (c) the region energy takes a small; (d) regional coefficient difference combined with average-gradient grayscale stretching (RCD-AG-GS). Figure 19: Comparison of different high-frequency horizontal component fusion rules: (**a**) maximum value of regional contrast; (**b**) average value of corresponding pixels; (**c**) minimum value of regional energy; (**d**) maximum value of weighted regional energy. Figure 20: Comparison of different high-frequency diagonal component fusion rules: (**a**) maximum value of regional contrast; (**b**) average value of corresponding pixels; (**c**) minimum value of regional energy; (**d**) maximum value of weighted regional energy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **MSE** & **PSNR** & **EN** & **MI** \\ \hline PI & 10.7134 & 37.8315 & 1.0850 & / \\ PF & 10.0424 & 37.8934 & 1.1052 & / \\ Maximum Value of & 10.5619 & 37.8934 & 1.1422 & 1.7644 \\ Regional Contrast & & & & \\ Average Value of & 9.5830 & 37.8552 & 1.2031 & 1.6510 \\ Corresponding Pixels & & & & \\ Minimum Value of & 13.8219 & 36.7251 & 1.0569 & 1.7961 \\ Regional Energy & & & & \\ Maximum Value of & 7.5514 & 39.3505 & 1.2660 & 2.0668 \\ Weighted Regional Energy & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of different high-frequency vertical component fusion rules. From the above fusion images of high-frequency vertical, horizontal, and diagonal components and objective evaluation function indexes, it can be seen that the objective evaluation function indexes of the image with the fusion rule of taking the maximum value of the high-frequency weighted regional energy are better than the source image and other fusion rules. The root mean square error is significantly reduced, and the peak signal-to-noise ratio, information entropy, and mutual information indexes are all improved. Subjectively, the image processed by the fusion rule selected in this paper is relatively clearer, and the details are more specific. Then, the inverse Haar wavelet transform is performed on the obtained low-frequency and high-frequency coefficients to complete the reconstruction of high- and low-frequency components, and the fused polarization image array is obtained as shown in Figure 21. The results after comparison with the average values of various indicators of the initial images with different polarization directions are shown in Table 6. In terms of objective indicators, the peak signal-to-noise ratio of the fused image is increased by 61.4%, the average gradient is increased by 47.3%, and the information entropy is increased by 8.5%, which fully shows that the fused image has more information and shows more details.