Denoising hyperspectral images (HSIs) is a crucial preprocessing procedure due to the noise originating from intra-imaging mechanisms and environmental factors. Utilizing domain-specific knowledge of HSIs, such as spectral correlation, spatial self-similarity, and spatial-spectral correlation, is essential for deep learning-based denoising. Existing methods are often constrained by running time, space complexity, and computational complexity, employing strategies that explore these priors separately. While these strategies can avoid some redundant information, they inevitably overlook broader and more underlying long-range spatial-spectral information that positively impacts image restoration. This paper proposes a Spatial-Spectral Selective State Space Model-based U-shaped network, termed Spatial-Spectral U-Mamba (SSUMamba), for hyperspectral image denoising. We can obtain complete global spatial-spectral correlation within a module thanks to the linear space complexity in State Space Model (SSM) computations. We introduce a Spatial-Spectral Alternating Scan (SSAS) strategy for HSIs, which helps model the information flow in multiple directions in 3-D HSIs. Experimental results demonstrate that our method outperforms compared methods. The source code is available at https://github.com/lronkitty/SSUMamba.

翻译：高光谱图像去噪是影像预处理中至关重要的环节，其噪声源于成像内部机制与环境因素。利用高光谱图像特有的领域知识（如光谱相关性、空间自相似性及空间-光谱相关性）对基于深度学习的去噪方法不可或缺。现有方法常受限于运行时间、空间复杂度与计算复杂度，采用分别探索这些先验信息的策略。尽管此类策略能避免部分冗余信息，却不可避免地忽略了更广泛且更基础的、对图像重建具有积极影响的长程空间-光谱信息。本文提出一种基于空间-光谱选择性状态空间模型的U型网络——简称空间-光谱U-Mamba（SSUMamba），用于高光谱图像去噪。得益于状态空间模型（SSM）计算的线性空间复杂度，我们可在单一模块内获取完整的全局空间-光谱相关性。针对高光谱数据引入了一种空间-光谱交替扫描（SSAS）策略，该策略有助于建模三维高光谱图像中多方向的信息流。实验结果表明，本方法性能优于对比方法。源代码可见于：https://github.com/lronkitty/SSUMamba。