## 3 Experimental Results and Analysis
### Experiments on Remote-Sensing HSI Datasets  
To comprehensively evaluate the robustness and denoising performance of the proposed network, systematic experiments were carried out on four representative hyperspec \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Case** & **Index** & **Noisy** & **LLRT** & **BM4D** & **MemNet** &
\begin{tabular}{l} **HSID-** \\ **CNN** \\ \end{tabular} & **SQAD** & **QRNN3D** & **MAFNet** & **Ours** \\ \hline \multirow{3}{*}{Case1} & PSNR & 17.97 & 32.71 & 35.86 & 36.75 & 38.75 & 42.32 & 42.71 & 42.91 & **43.45** \\  & SSIM & 0.162 & 0.823 & 0.867 & 0.932 & 0.948 & 0.954 & 0.958 & 0.963 & **0.967** \\  & SAM & 0.864 & 0.182 & 0.121 & 0.102 & 0.090 & 0.051 & 0.047 & 0.045 & **0.041** \\ \hline \multirow{3}{*}{Case2} & PSNR & 17.58 & 30.79 & 34.53 & 37.11 & 38.98 & 41.92 & 42.35 & 42.65 & **43.17** \\  & SSIM & 0.151 & 0.785 & 0.845 & 0.935 & 0.949 & 0.949 & 0.955 & 0.962 & **0.965** \\  & SAM & 0.878 & 0.208 & 0.144 & 0.098 & 0.084 & 0.051 & 0.053 & 0.048 & **0.044** \\ \hline \multirow{3}{*}{Case3} & PSNR & 17.44 & 28.76 & 31.97 & 38.34 & 39.02 & 40.86 & 40.92 & 42.16 & **42.55** \\  & SSIM & 0.146 & 0.722 & 0.812 & 0.935 & 0.942 & 0.945 & 0.954 & 0.961 & **0.963** \\  & SAM & 0.894 & 0.218 & 0.178 & 0.111 & 0.088 & 0.061 & 0.063 & 0.050 & **0.045** \\ \hline \multirow{3}{*}{Case4} & PSNR & 14.92 & 26.41 & 28.71 & 35.10 & 35.34 & 39.39 & 39.48 & 39.92 & **40.50** \\  & SSIM & 0.121 & 0.656 & 0.714 & 0.858 & 0.901 & 0.939 & 0.942 & 0.947 & **0.951** \\  & SAM & 0.905 & 0.287 & 0.241 & 0.203 & 0.173 & 0.087 & 0.091 & 0.084 & **0.074** \\ \hline \multirow{3}{*}{Case5} & PSNR & 14.09 & 23.26 & 27.54 & 34.82 & 35.17 & 38.71 & 39.07 & 39.67 & **40.28** \\  & SSIM & 0.093 & 0.569 & 0.692 & 0.841 & 0.898 & 0.934 & 0.941 & 0.945 & **0.950** \\  & SAM & 0.925 & 0.319 & 0.259 & 0.184 & 0.177 & 0.081 & 0.085 & 0.079 & **0.071** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of various methods under five complex noise scenarios on the ICVL dataset. Bold values indicate the best results for the corresponding metric. Figure 7: Complex noise removal results on the ICVL dataset. Examples for non-i.i.d Gaussian noise (Case 1), Gaussian + stripes (Case 2), Gaussian + deadline (Case 3), Gaussian + impulse (Case 4) and mixture noise (Case 5) removal are illustrated, respectively. tral datasets. These datasets cover diverse sensor types, spatial resolutions, and land cover characteristics. The Pavia University dataset [40] is employed for testing. This dataset, collected by the ROSIS-3 airborne sensor (German Aerospace Center--DLR, Oberpfaffenhofen, Germany), includes 103 valid spectral bands and has a spatial resolution of 610 \(\times\) 340 pixels, covering typical urban land cover in the city of Pavia, Italy. Next, the Washington DC Mall dataset [41], acquired using the HYDICE imaging spectrometer (Naval Research Laboratory, Washington, DC, USA), which contains 1208 \(\times\) 307 spatial size and 191 spectral bands. It provides a detailed record of the complex urban features of the National Mall and its surroundings. We clip a region with the sizes of 304 \(\times\) 304 \(\times\) 191 and 344 \(\times\) 344 \(\times\) 103 for testing. Meanwhile, the remaining parts of these two datasets are divided into overlapping patches with spatial size of 128 \(\times\) 128. We then obtain 800 patches and 400 patches in total for the Washington DC Mall and Pavia University datasets, respectively, which are used to fine-tune the pretrained model on the ICVL dataset. To further assess the network's performance in real-world noise conditions, two datasets of practical significance were chosen: the Urban dataset [42] and the Indian Pines dataset [43]. The Urban dataset contains 210 contiguous spectral bands, spanning a spectral range of 400 to 2500 nm, providing a comprehensive representation of the spectral characteristics of urban environments.