Denoising is a crucial preprocessing procedure for hyperspectral images (HSIs) due to the noise originating from intra-imaging mechanisms and environmental factors. Utilizing domain 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 kinds of domain knowledge separately. While these strategies can avoid some redundant information, they inevitably overlook broader and more in-depth long-range spatial-spectral information that positively impacts image restoration. This paper proposes a Spatial-Spectral Selective State Space Model-based U-shaped network, Spatial-Spectral U-Mamba (SSUMamba), for hyperspectral image denoising. The SSUMamba can exploit 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 Zigzag Scan (SSAZS) strategy for HSIs, which helps exploit the continuous information flow in multiple directions of 3-D characteristics within HSIs. Experimental results demonstrate that our method outperforms comparison methods. The source code is available at https://github.com/lronkitty/SSUMamba.

翻译：去噪是高光谱图像（HSI）至关重要的预处理步骤，因为噪声源自成像内部机制和环境因素。利用高光谱图像的领域知识，如光谱相关性、空间自相似性和空间-光谱相关性，对于基于深度学习的去噪方法至关重要。现有方法常受限于运行时间、空间复杂度和计算复杂度，通常采用分别探索这些领域知识的策略。虽然这些策略可以避免一些冗余信息，但它们不可避免地忽略了更广泛、更深入的、对图像复原有积极影响的长程空间-光谱信息。本文提出了一种基于空间-光谱选择性状态空间模型的U形网络——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）计算中的线性空间复杂度，SSUMamba可以在一个模块内利用完整的全局空间-光谱相关性。我们为高光谱图像引入了一种空间-光谱交替之字形扫描（SSAZS）策略，该策略有助于利用高光谱图像三维特征中多个方向的连续信息流。实验结果表明，我们的方法优于对比方法。源代码可在 https://github.com/lronkitty/SSUMamba 获取。