  Denoising is a crucial preprocessing step for hyperspectral images (HSIs) due
to noise arising from intra-imaging 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.
