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 the strategies can avoid some redundant information, considering that hyperspectral images are 3-D images with strong spatial continuity and spectral correlation, this kind of strategy inevitably overlooks subtle 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 an Alternating Scan (SSAS) strategy for HSI data, which helps model the information flow in multiple directions in 3-D HSIs. Experimental results demonstrate that our method outperforms several compared methods. The source code will be available at https://github.com/lronkitty/SSUMamba.

翻译：高光谱图像去噪是成像机制及环境因素引入噪声后至关重要的预处理步骤。利用高光谱图像的领域特定知识（如光谱相关性、空间自相似性以及空间-光谱相关性）对于基于深度学习的去噪方法至关重要。现有方法常受运行时间、空间复杂度和计算复杂度的限制，采用分别探索这些先验知识的策略。虽然这些策略可避免部分冗余信息，但考虑到高光谱图像是具有强空间连续性与光谱相关性的三维图像，此类策略不可避免地忽略了有益于图像恢复的细微长程空间-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，称为Spatial-Spectral U-Mamba（SSUMamba），用于高光谱图像去噪。得益于状态空间模型计算的线性空间复杂度，我们可在单一模块内获得完整的全局空间-光谱相关性。针对高光谱数据引入交替扫描策略，该策略有助于建模三维高光谱图像中多方向的信息流动。实验结果表明，本方法优于多种对比方法。源代码将发布于https://github.com/lronkitty/SSUMamba。