## 3 Results
The models trained by deep learning algorithms in high noise environments only include standard deviations of 0\(\sim\)75, and there are limited data, making it difficult to train better models in higher noise environments. The algorithm in this paper is based on the fact that the proposed method is able to achieve better PSNR and SSIM in low-noise environments. Figure 11: Denoising results with noise std50 in image(c). Figure 12: Denoising results with noise std75 in image(c). Figure 13: Denoising results with noise std100 in image(c). is unaffected by the amount of data, and as the complexity of the environment increases, it still achieves good PSNR and SSIM, demonstrating that the algorithm in this paper has stronger environmental adaptability. Finally, we compute the average local SCRG of the target in the IR sequences containing small targets. The results are shown in Table 8. It can be seen that the algorithm in this paper still improves the texture clarity of the image in high Gaussian noise environments. We have obtained a higher local SCRG of infrared small targets, which constructs the foundation for subsequent high-performance target detection. It has further verified the denoising performance of the algorithm. Through the above results, we have proven that our method improves the PSNR, SSIM, and mean local SCRG of small targets among all test images compared with traditional methods under high-noise environments. Additionally, our method could be adaptable to a complex background and high-noise environments. It would lead to a better target detection effect for remote sensing. \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 & 32.032 & 0.864 & 27.151 & 0.541 & 24.179 & 0.357 & 21.88 & 0.243 \\ \hline Gaussian & 32.40 & 0.86 & 27.00 & 0.54 & 23.90 & 0.36 & 21.56 & 0.24 \\ \hline BM3D & 42.197 & 0.977 & 37.935 & 0.958 & 35.577 & 0.935 & 33.777 & 0.908 \\ \hline EPLL & 40.707 & 0.96 & 34.501 & 0.915 & 32.014 & 0.89 & 30.417 & 0.84 \\ \hline NSCR & 42.519 & 0.982 & 38.412 & 0.970 & 36.657 & 0.960 & 35.060 & 0.949 \\ \hline KSVD & 37.955 & 0.917 & 33.098 & 0.843 & 30.310 & 0.775 & 28.032 & 0.693 \\ \hline FFDNet & 43.62 & 0.983 & 39.47 & 0.969 & 32.21 & 0.954 & / & / \\ \hline ADNet & 41.77 & 0.976 & 38.4 & 0.949 & 16.5 & 0.049 & / & / \\ \hline GSR-WNNM & 41.581 & 0.974 & 37.296 & 0.949 & 35.916 & 0.931 & 34.549 & 0.907 \\ \hline Proposed & 41.987 & 0.975 & 38.672 & 0.959 & 37.268 & 0.962 & 36.382 & 0.961 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison results of PSNR and SSIM with different denoising methods in Image(b). \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 & 32.134 & 0.867 & 27.22 & 0.541 & 24.21 & 0.353 & 21.825 & 0.239 \\ \hline Gaussian & 32.44 & 0.86 & 27.06 & 0.54 & 23.91 & 0.35 & 21.49 & 0.24 \\ \hline BM3D & 42.645 & 0.977 & 38.567 & 0.962 & 36.277 & 0.942 & 34.314 & 0.917 \\ \hline EPLL & 41.563 & 0.966 & 35.11 & 0.958 & 32.301 & 0.899 & 30.653 & 0.819 \\ \hline NSCR & 38.690 & 0.921 & 33.414 & 0.848 & 30.516 & 0.778 & 28.283 & 0.711 \\ \hline KSVD & 42.970 & 0.982 & 39.365 & 0.977 & 37.456 & 0.969 & 36.063 & 0.959 \\ \hline FFDNet & 44.59 & 0.983 & 40.59 & 0.974 & 38.01 & 0.964 & / & / \\ \hline ADNet & 42.35 & 0.975 & 39.51 & 0.964 & 16.48 & 0.046 & / & / \\ \hline GSR-WNNM & 41.76 & 0.974 & 38.158 & 0.956 & 36.678 & 0.943 & 35.019 & 0.915 \\ \hline Proposed & 42.451 & 0.975 & 38.938 & 0.963 & 38.04 & 0.971 & 37.121 & 0.971 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison results of PSNR and SSIM with different denoising methods in Image(a).