  Deep unfolding methods have made impressive progress in restoring 3D
hyperspectral images (HSIs) from 2D measurements through convolution neural
networks or Transformers in spectral compressive imaging. However, they cannot
efficiently capture long-range dependencies using global receptive fields,
which significantly limits their performance in HSI reconstruction. Moreover,
these methods may suffer from local context neglect if we directly utilize
Mamba to unfold a 2D feature map as a 1D sequence for modeling global
long-range dependencies. To address these challenges, we propose a novel Dual
Hyperspectral Mamba (DHM) to explore both global long-range dependencies and
local contexts for efficient HSI reconstruction. After learning informative
parameters to estimate degradation patterns of the CASSI system, we use them to
scale the linear projection and offer noise level for the denoiser (i.e., our
proposed DHM). Specifically, our DHM consists of multiple dual hyperspectral S4
blocks (DHSBs) to restore original HSIs. Particularly, each DHSB contains a
global hyperspectral S4 block (GHSB) to model long-range dependencies across
the entire high-resolution HSIs using global receptive fields, and a local
hyperspectral S4 block (LHSB) to address local context neglect by establishing
structured state-space sequence (S4) models within local windows. Experiments
verify the benefits of our DHM for HSI reconstruction. The source codes and
models will be available at https://github.com/JiahuaDong/DHM.
