## 3 Experimental Results and Analysis
### Experiments on Gaussian Noise Cases  
This paper employs a single unified network to handle different levels of Gaussian noise. Specifically, additive Gaussian noise with varying variances is introduced to create a set of noisy hyperspectral image. The average evaluation metrics are presented in Table 1, with the best performances for each metric highlighted in bold. Figure 5 presents the denoising results for a noise level of \(\sigma=50\), while Figure 6 displays the results for blind noise, offering a detailed comparison of the outcomes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Case** & **Index** & **Noisy** & **LLRT** & **BM4D** & **MemNet** &
\begin{tabular}{c} **HSID-** \\ **CNN** \\ \end{tabular} & **SQAD** & **QRNN3D** & **MAFNet** & **Ours** \\ \hline \multirow{3}{*}{\(\sigma=30\)} & PSNR & 18.51 & 40.72 & 37.16 & 40.52 & 39.65 & 43.92 & 43.97 & 44.56 & **45.31** \\  & SSIM & 0.130 & 0.946 & 0.908 & 0.943 & 0.948 & 0.966 & 0.968 & 0.975 & **0.978** \\  & SAM & 0.751 & 0.072 & 0.093 & 0.085 & 0.092 & 0.059 & 0.056 & 0.044 & **0.038** \\ \hline \multirow{3}{*}{\(\sigma=50\)} & PSNR & 16.43 & 37.94 & 35.86 & 39.79 & 38.89 & 41.62 & 41.78 & 42.12 & **43.19** \\  & SSIM & 0.053 & 0.921 & 0.867 & 0.936 & 0.929 & 0.957 & 0.956 & 0.959 & **0.965** \\  & SAM & 0.847 & 0.098 & 0.118 & 0.084 & 0.091 & 0.061 & 0.059 & 0.053 & **0.044** \\ \hline \multirow{3}{*}{\(\sigma=70\)} & PSNR & 14.84 & 35.33 & 32.49 & 36.46 & 38.67 & 40.71 & 40.49 & 41.39 & **42.07** \\  & SSIM & 0.036 & 0.893 & 0.842 & 0.898 & 0.922 & 0.947 & 0.945 & 0.953 & **0.957** \\  & SAM & 0.993 & 0.137 & 0.158 & 0.099 & 0.121 & 0.059 & 0.065 & 0.052 & **0.046** \\ \hline \multirow{3}{*}{Blind} & PSNR & 17.47 & 34.28 & 35.25 & 37.80 & 39.69 & 41.94 & 42.25 & 42.83 & **43.41** \\  & SSIM & 0.084 & 0.887 & 0.862 & 0.932 & 0.941 & 0.954 & 0.957 & 0.963 & **0.966** \\ \cline{1-1}  & SAM & 0.862 & 0.114 & 0.131 & 0.101 & 0.116 & 0.052 & 0.057 & 0.046 & **0.043** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of different methods on the ICVL dataset. Note: “Blind” indicates that each image is corrupted by Gaussian noise with unknown \(\sigma\) Bold values indicate the best results for the corresponding metric.  
Figure 5: The Gaussian noise removing results of SSIM at 13 th band of the HSI under noise level \(\sigma=50\) (ICVL dataset).  
Experimental comparisons clearly show that the network proposed in this paper demonstrates superior denoising performance with respect to Gaussian noise, as evidenced by improvements in key metrics such as PSNR, SSIM, and SAM. This performance advantage is primarily due to the network's enhanced integration of multi-scale contextual information and the synergistic effect of the spatial-spectral attention mechanism, which strengthens its feature extraction capability. Compared to existing methods like MAFNet, HSID-CNN, SQAD, and QRNN3D, the proposed network not only suppresses noise more effectively but also retains texture details in the image better. The denoising results, visualized in Figures 5 and 6, show that traditional methods such as LLRT perform poorly when dealing with strong Gaussian noise, while other deep learning methods, although effective at noise suppression, still suffer loss of texture detail. In contrast, the proposed network more effectively preserves details in textures like tree branches and stone columns. Quantitative analysis further verifies the superiority of the proposed network, with its PSNR and SSIM scores outperforming those of the other methods.