SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising

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.