## 1 Introduction
[37] introduced the Spatial-Spectral Attention Recursive Network (SQAD), which models the relationships between spatial and spectral features to a certain degree by incorporating an attention mechanism. The experimental results demonstrate that this approach can effectively enhance feature fusion efficiency. However, this high-level feature interaction method still fails to fully capture the spatial-spectral interdependencies unique to hyperspectral data, limiting the network's generalization ability in complex scenarios. Additionally, traditional feature transfer paths often follow fixed patterns, hindering the adaptive adjustment of fusion weights for spectral and spatial features, further constraining the model's representational capability. In multi-scale feature processing, although the multi-scale adaptive fusion network (MAFNet) proposed by [PERSON] et al. [38] increases the network's receptive field by constructing a multi-level feature pyramid, the traditional downsampling techniques it employsinevitably result in information loss. This is particularly problematic when processing high-frequency details and fine spectral characteristics, as conventional pooling or convolutional downsampling often cause feature blurring and spectral distortion. Additionally, most current multi-scale fusion strategies rely on simple feature concatenation or addition, which makes it difficult to achieve truly effective complementarity between features across scales. To some extent, this limitation restricts the potential for improving the model's denoising performance. To address the challenges of insufficient collaborative representation between spectral and spatial features and the loss of information during multi-scale fusion, this study proposes a hyperspectral image denoising network (MDSSANet). The network incorporates an adaptive spatial-spectral collaborative attention mechanism, which utilizes a parallel structure with two branches for spectral and spatial features, enabling joint optimization of multi-dimensional features. For multi-scale feature processing, this study introduces a differentiated strategy for multi-scale feature fusion. This strategy constructs parallel processing branches at large, medium, and small scales and employs dynamic weight-based feature fusion, thereby achieving hierarchical modeling of spatial-spectral features and mitigating the loss of feature information during multi-scale upsampling and downsampling. The contributions of this work can be summarized as follows:  
1. We propose a hyperspectral image denoising network (MDSSANet), which constructs a multi-scale differentiated denoising network based on spatial-spectral cooperative attention, achieving the collaborative optimization of spatial-spectral features and the effective fusion of multi-scale information. Experiments on multiple standard datasets demonstrate the effectiveness and rationality of the proposed MDSSANet. 2. A spectral-spatial cooperative attention (SSCA) module is optimized by implementing the parallel spectral channel attention and spatial attention mechanisms, and the fusion weights of the two types of features are adjusted in combination with 3D convolution. This approach enables adaptive optimization of cross-band features and precise modeling of long-range spatial dependencies, thereby enhancing the network's ability to extract features of multi-scale objects, especially small targets. 3. We design a differentiated multi-scale feature fusion module (DMF) to conduct cross-scale skip connections and differentiation processing strategy in the multi-scale fusion process. A dynamic fusion mechanism is employed to capture spatial-spectral features at different scales using dynamic weighting, which helps alleviate the issue of information loss commonly encountered in traditional multi-scale fusion.