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 will be available at https://github.com/lronkitty/SSUMamba.

翻译：高光谱图像（HSI）的去噪是由于成像机制和环境因素引入噪声的关键预处理步骤。利用高光谱图像的领域特定知识（如光谱相关性、空间自相似性及空间-光谱相关性）对基于深度学习的去噪方法至关重要。现有方法常受限于运行时间、空间复杂度和计算复杂度，通过分别探索这些先验信息的策略实现去噪。尽管这些策略能避免部分冗余信息，但不可避免地忽略了更广泛且深层的、对图像恢复有益的长程空间-光谱信息。本文提出一种基于状态空间模型的U型网络——空间-光谱U-Mamba（SSUMamba），用于高光谱图像去噪。借助状态空间模型（SSM）计算中的线性空间复杂度，我们可在单一模块内获取完整的全局空间-光谱相关性。针对高光谱图像，我们引入了空间-光谱交替扫描（SSAS）策略，该策略有助于模拟三维HSI中多方向的信息流。实验结果表明，本方法优于对比方法。源代码将发布于https://github.com/lronkitty/SSUMamba。