  Effectively discerning spatial-spectral dependencies in HSI denoising is
crucial, but prevailing methods using convolution or transformers still face
computational efficiency limitations. Recently, the emerging Selective State
Space Model(Mamba) has risen with its nearly linear computational complexity in
processing natural language sequences, which inspired us to explore its
potential in handling long spectral sequences. In this paper, we propose
HSIDMamba(HSDM), tailored to exploit the linear complexity for effectively
capturing spatial-spectral dependencies in HSI denoising. In particular, HSDM
comprises multiple Hyperspectral Continuous Scan Blocks, incorporating
BCSM(Bidirectional Continuous Scanning Mechanism), scale residual, and spectral
attention mechanisms to enhance the capture of long-range and local
spatial-spectral information. BCSM strengthens spatial-spectral interactions by
linking forward and backward scans and enhancing information from eight
directions through SSM, significantly enhancing the perceptual capability of
HSDM and improving denoising performance more effectively. Extensive
evaluations against HSI denoising benchmarks validate the superior performance
of HSDM, achieving state-of-the-art results in performance and surpassing the
efficiency of the latest transformer architectures by $30\%$.
