## 3 Results
The simulation results show that the improved algorithm proposed in this paper can adapt to lower SNR infrared images. It improves the PSNR of images, restores image details efficiently, and ensures a higher SSIM of images. Moreover, we provide some annotations about the flight target in the IR images. We achieve these IR sequences with IR cool mid-wave detector \(CMS6055\) through outdoor experiments. It occupies the 3\(\sim\)5 um mid-wave infrared band and produces \(640*512\) resolution image sequences. The single pixel size is 15 um. The target is not larger than \(3*3\) pixels. Each sequence has about 20 frames. With the different std noise, the effect of the denoising of image(a) is depicted in Figures 2-5. The effect of the denoising of image(b) is shown in Figures 6-9. The effect of the denoising of image(c) is displayed in Figures 10-13. It can be seen that our method has a better denoising effect under different complexity backgrounds. Under a high-noise environment, we still have a better recovery effect compared to traditional methods and deep learning methods. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Noise Std** & \multicolumn{2}{c}{**20**} & \multicolumn{2}{c}{**50**} & \multicolumn{2}{c}{**75**} & \multicolumn{2}{c}{**100**} \\ \hline Metrics & PSNR & SSIM & PSNR & SSIM & PSNR & SSIM & PSNR & SSIM \\ \hline Mean & 24.723 & 0.710 & 23.360 & 0.575 & 21.868 & 0.468 & 20.375 & 0.386 \\ \hline Gaussian & 25.596 & 0.747 & 23.817 & 0.608 & 22.068 & 0.500 & 20.471 & 0.412 \\ \hline BM3D & 31.412 & 0.891 & 26.740 & 0.767 & 24.818 & 0.694 & 23.474 & 0.637 \\ \hline EPLL & 30.861 & 0.863 & 26.323 & 0.745 & 24.427 & 0.690 & 23.229 & 0.649 \\ \hline NSCR & 30.837 & 0.871 & 25.596 & 0.708 & 23.257 & 0.605 & 21.918 & 0.535 \\ \hline KSVD & 31.327 & 0.884 & 26.336 & 0.751 & 24.364 & 0.683 & 23.170 & 0.637 \\ \hline FFDNet & 31.31 & 0.933 & 26.8 & 0.85 & 24.9 & 0.70 & / & / \\ \hline ADNet & 31.16 & 0.880 & 26.769 & 0.769 & 16.755 & 0.342 & / & / \\ \hline GSR-WNNM & 31.509 & 0.889 & 26.926 & 0.776 & 24.91 & 0.703 & 23.503 & 0.651 \\ \hline Proposed & 31.669 & 0.895 & 26.937 & 0.775 & 24.916 & 0.705 & 23.639 & 0.653 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison results of PSNR and SSIM with different denoising methods in image \((3)\). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Noise Std** & \multicolumn{2}{c}{**20**} & \multicolumn{2}{c}{**50**} & \multicolumn{2}{c}{**75**} & \multicolumn{2}{c}{**100**} \\ \hline Metrics & PSNR & SSIM & PSNR & SSIM & PSNR & SSIM & PSNR & SSIM \\ \hline Mean & 22.59 & 0.76 & 21.69 & 0.63 & 20.66 & 0.53 & 19.49 & 0.45 \\ \hline Gaussian & 23.49 & 0.80 & 22.24 & 0.65 & 20.93 & 0.55 & 19.63 & 0.46 \\ \hline BM3D & 30.35 & 0.92 & 25.82 & 0.82 & 23.91 & 0.75 & 22.52 & 0.69 \\ \hline EPLL & 30.48 & 0.93 & 25.76 & 0.8 & 23.72 & 0.73 & 22.12 & 0.67 \\ \hline NSCR & 30.62 & 0.92 & 25.65 & 0.82 & 23.54 & 0.76 & 22.21 & 0.71 \\ \hline KSVD & 29.98 & 0.91 & 25.31 & 0.79 & 22.90 & 0.72 & 20.88 & 0.64 \\ \hline FFDNet & 31.19 & 0.83 & 27.31 & 0.72 & 24.26 & 0.66 & / & / \\ \hline ADNet & 31.29 & 0.833 & 26.89 & 0.721 & 17.27 & 0.205 & / & / \\ \hline GSR-WNNM & 30.975 & 0.923 & 26.226 & 0.829 & 24.284 & 0.775 & 22.858 & 0.724 \\ \hline Proposed & 31.078 & 0.926 & 26.241 & 0.830 & 24.350 & 0.778 & 22.897 & 0.731 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results of PSNR and SSIM with different denoising methods in image \((2)\). We obtained a comparison of PSNR and SSIM in Tables 5-7 with three different complexity image sequences. Compared with traditional algorithms, our algorithm achieves better PSNR and SSIM in all image sequences and has good environmental adaptability. Compared with typical deep learning algorithms, our algorithm has slightly lower PSNR and SSIM in low-noise environments compared to the FFDNet algorithm, which is equivalent to the ADNet algorithm. In high-noise environments, we have achieved higher PSNR and SSIM.