## 3 Experimental Results and Analysis
### Experiments on Remote-Sensing HSI Datasets
#### 3.4.2 Washington DC Mall  
To further assess the network's denoising effectiveness, severe noise was introduced into the Washington DC Mall dataset, with the experimental results detailed in Table 4. As shown in the table, the proposed network attains the best performance across all quantitative evaluation metrics. The relevant visual outcomes are presented in Figure 11. Compared with the spectral distortion after noise reduction by HSID-CNN, the images after noise reduction in this paper not only retain the spectral information more effectively, but the texture structures of building roofs and road edges are also closer to those of the real images.  
As Figure 12 shows, that performance varies between spectral bands. In some bands, the denoised images still exhibit distortion, which can be attributed to several factors. For example, the noise characteristics vary across spectral bands, with some bands experiencing stronger noise and others being less affected, leading to differences in denoising results. Furthermore, sensor sensitivity differs between bands, and high-frequency information in hyperspectral images is more susceptible to noise, resulting in varying denoising effectiveness. However, in most spectral bands, the proposed network outperforms other methods, effectively removing noise while preserving original details. The zoomed-in visualizations show that after denoising with the proposed network, the land cover details on building rooftops are better preserved.  
As illustrated in Figure 13, certain bands in the 171 st band are heavily contaminated by noise, causing significant distortion in some pixels of the image. While most networks can restore the noisy image to a DN value curve that roughly follows the original, the results indicate that the L LRT fails to accurately reconstruct the global curve. Although deep-learning-based methods recover the general trend of the DN values, the proposed network not only reconstructs the true shape of the curve more accurately but also preserves details more effectively, resulting in restored data that are closer to the ground truth.  
Figure 10: Horizontal mean digital number curves at 30 th band of real noise by different methods on the Pavia University dataset. (**a**) Ground Truth, (**b**) Noise, (**c**) LRT, (**d**) BM4D, (**e**) MEMNet, (**f**) HSID-CNN, (**g**) SQAD, (**h**) QRNN3D, (i) MAFNet, (j) Ours.  
\begin{table}
\begin{tabular}{c 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 & 14.43 & 19.06 & 21.97 & 23.51 & 22.30 & 24.51 & 25.91 & 26.34 & **26.68** \\ SSIM & 0.127 & 0.537 & 0.628 & 0.787 & 0.804 & 0.864 & 0.868 & 0.881 & **0.891** \\ SAM & 0.902 & 0.351 & 0.255 & 0.128 & 0.135 & 0.098 & 0.101 & 0.095 & **0.092** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of various methods on the Washington DC Mall dataset. Bold values indicate the best results for the corresponding metric.  
Figure 11: Simulated noise removal results of SSIM at 16 th, 26 th and 59 th bands of mixture noise on the Washington DC Mall dataset. (**a**) Ground Truth, (**b**) Noise, (**c**) LLRT, (**d**) BM4D, (**e**) MEMNet, (**f**) HSID-CNN, (**g**) SQAD, (h) QRNN3D, (**i**) MAFNet, (**j**) Ours.  
Figure 12: PSNR and SSIM results of different denoising methods for each spectral band in the simulated mixture noise experiments on the Washington DC Mall dataset. (**a**) SSIM performance across spectral bands. (**b**) PSNR performance across spectral bands.