Denoising is a crucial preprocessing step for hyperspectral images (HSIs) due to noise arising from intraimaging mechanisms and environmental factors. Long-range spatial-spectral correlation modeling is beneficial for HSI denoising but often comes with high computational complexity. Based on the state space model (SSM), Mamba is known for its remarkable long-range dependency modeling capabilities and computational efficiency. Building on this, we introduce a memory-efficient spatial-spectral UMamba (SSUMamba) for HSI denoising, with the spatial-spectral continuous scan (SSCS) Mamba being the core component. SSCS Mamba alternates the row, column, and band in six different orders to generate the sequence and uses the bidirectional SSM to exploit long-range spatial-spectral dependencies. In each order, the images are rearranged between adjacent scans to ensure spatial-spectral continuity. Additionally, 3D convolutions are embedded into the SSCS Mamba to enhance local spatial-spectral modeling. Experiments demonstrate that SSUMamba achieves superior denoising results with lower memory consumption per batch compared to transformer-based methods. The source code is available at https://github.com/lronkitty/SSUMamba.

翻译：由于成像机制与环境因素引入的噪声，去噪是高光谱图像（HSI）至关重要的预处理步骤。长程空间-光谱相关性建模对HSI去噪有益，但通常伴随较高的计算复杂度。基于状态空间模型（SSM），Mamba以其卓越的长程依赖建模能力和计算效率而闻名。在此基础上，我们提出了一种内存高效的空间-光谱UMamba（SSUMamba）用于HSI去噪，其核心组件为空间-光谱连续扫描（SSCS）Mamba。SSCS Mamba以六种不同顺序交替扫描行、列和波段以生成序列，并利用双向SSM来挖掘长程空间-光谱依赖关系。在每种扫描顺序中，图像在相邻扫描间被重新排列以确保空间-光谱连续性。此外，我们在SSCS Mamba中嵌入了3D卷积以增强局部空间-光谱建模。实验表明，与基于Transformer的方法相比，SSUMamba在每批次内存消耗更低的情况下实现了更优的去噪效果。源代码发布于 https://github.com/lronkitty/SSUMamba。