## 3 Experimental Results and Analysis
### Experiments on Remote-Sensing HSI Datasets
#### 3.4.1 Pavia University  
Mixed noise was applied to the Pavia University dataset in this study, and the experimental results are summarized in Table 3. The results show that the proposed network achieves the best performance in terms of the quantitative evaluation metrics. The corresponding visual results are presented in Figure 8. This method effectively removes complex noise while preserving high-frequency texture details in the image. The denoised image retains spectral information more effectively and better preserves the texture structure of the metal plate ground and surrounding small buildings.  
Figure 9 shows the PSNR and SSIM values of the restored hyperspectral images for different bands in the dataset. The proposed method achieves the highest PSNR values across nearly all spectral bands. It is observed that most methods show varying performance across spectral bands, primarily due to the varying intrinsic noise levels in each band, resulting in different denoising challenges. Additionally, due to the characteristics of hyperspectral sensors, spatial details vary across spectral bands. As a result, nearly all methods perform differently across the spectral bands.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Index** & **Noisy** & **LLRT** & **BM4D** & **MemNet** &
\begin{tabular}{c} **HSID-** \\ **CNN** \\ \end{tabular} & **SQAD** & **QRNN3D** & **MAFNet** & **Ours** \\ \hline PSNR & 17.67 & 21.09 & 25.97 & 30.78 & 29.61 & 32.91 & 33.26 & 33.73 & **34.14** \\ SSIM & 0.139 & 0.578 & 0.658 & 0.811 & 0.823 & 0.882 & 0.889 & 0.908 & **0.917** \\ SAM & 0.872 & 0.321 & 0.275 & 0.150 & 0.161 & 0.121 & 0.126 & 0.113 & **0.104** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of different methods on the Pavia University dataset. Bold values indicate the best results for the corresponding metric.  
Figure 10 shows the average normalized digital number (DN) curves for different methods. It is evident that the image is significantly affected by noise across the 30 bands. Although most methods can restore the denoised image to the original trend, the traditional low-rank tensor recovery model (LLRT) has a large deviation. However, this network can more accurately reconstruct the DN value damaged by noise and minimize the denoising deviation. Furthermore, compared to recent deep learning networks such as SQAD and MAFNet, the proposed method is more stable in preserving spectral information, resulting in denoised DN values that are closer to the original data distribution. This further demonstrates that the proposed network effectively denoises without causing significant spectral distortion.  
Figure 8: Simulated noise removal results of SSIM at 3 rd, 13 th and 23 rd bands of mixture noise on the Pavia University dataset. (**a**) Ground Truth, (**b**) Noise, (**c**) LLRT, (**d**) BM4D, (**e**) MEMNet, (**f**) HSID-CNN, (**g**) SQAD, (**h**) QRNN3D, (**i**) MAFNet, (**j**) Ours.  
Figure 9: PSNR and SSIM results of different denoising methods for each spectral band in the simulated mixture noise experiments on the Pavia University dataset. (**a**) SSIM performance across spectral bands. (**b**) PSNR performance across spectral bands.