## 4 Conclusions  
This paper proposes multi-scale differentiated network with spatial-spectral cooperative attention for hyperspectral image denoising (MDSSANet). The network includes two core modules: the spatial-spectral cooperative attention (SSCA) module, which enhances cross-band features adaptively and precisely models long-range spatial dependencies through parallel spectral and spatial attention mechanisms; and the differentiated multi-scale feature fusion (DMF) module, which uses a three-branch parallel architecture to effectively alleviate the loss of information in multi-scale feature transmission by dynamically weighting and fusing large-scale feature extraction, medium-scale spatial-spectral  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Noisy** & **Ours-Two** & **MAFNet** & **Ours-Three** & **Ours** \\ \hline PSNR & 17.67 & 32.12 & 33.73 & 33.85 & **34.14** \\ SSIM & 0.139 & 0.893 & 0.908 & 0.910 & **0.917** \\ SAM & 0.872 & 0.121 & 0.113 & 0.109 & **0.104** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation of the number of downsampling.  
Figure 16: Input features, spatial features, spectral features, and feature maps after the multiplication of the spatial–spectral feature map.  
Figure 17: Simulated noise removal results of SSIM at 1 st, 71 st and 101 st band of mixture noise on the Pavia University dataset. Bold values indicate the best results for the corresponding metric.  
joint modeling, and small-scale fine feature enhancement. The experimental results of Case 5 complex noise on several synthetic and real hyperspectral datasets (ICVL, Pavia University and Washington DC Mall) show that the proposed method outperforms existing approaches across multiple objective evaluation metrics: PSNR increases by 0.45 dB on average, SSIM by 0.8%, and SAM by 0.7%. In particular, the method demonstrates superior performance in complex noise environments, such as Gaussian-impulse mixed noise, and in small object restoration tasks, such as agricultural field boundaries and urban buildings, confirming its effectiveness in real-world application scenarios.  
Considering the limitations of the current research, future work will focus on the following two optimization areas: first, to address the computational complexity of the multi-scale architecture, lightweight network designs using depthwise separable convolution and knowledge distillation will be explored; second, research will investigate a noise distribution adaptation mechanism based on meta-learning, along with dynamic network pruning strategies, to enhance the model's generalization ability for heterogeneous sensor data.  
Conceptualization, X.W. and L.C.; methodology, X.W. and X.C.; validation, X.C. and X.H.; formal analysis, M.Y.; investigation, X.H. and M.Y.; data curation, X.C.; writing--original draft preparation, X.W.; writing--review and editing, L.C. and X.C.; Visualization, M.Y.; supervision, L.C.; project administration, M.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hubei Province under grant No. 2022 CFB447.  
Not applicable.  
The datasets used and/or analyzed during the current study are publicly available and can be accessed without any restrictions. If needed, they can also be obtained by contacting the corresponding author.  
The authors declare no conflicts of interest.