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 this https URL

本论文提出了一种基于空间 - 谱选择性状态空间模型的 U 型网络（称为 SSUMamba），用于高光谱图像去噪，能够在状态空间模型计算中实现完全的全局空间 - 谱相关性，通过引入交替扫描策略（SSAS）对多方向 3D 高光谱图像进行信息流建模。实验证明，我们的方法优于其他方法。