  Mainstream approaches to spectral reconstruction (SR) primarily focus on
designing Convolution- and Transformer-based architectures. However, CNN
methods often face challenges in handling long-range dependencies, whereas
Transformers are constrained by computational efficiency limitations. Recent
breakthroughs in state-space model (e.g., Mamba) has attracted significant
attention due to its near-linear computational efficiency and superior
performance, prompting our investigation into its potential for SR problem. To
this end, we propose the Gradient-guided Mamba for Spectral Reconstruction from
RGB Images, dubbed GMSR-Net. GMSR-Net is a lightweight model characterized by a
global receptive field and linear computational complexity. Its core comprises
multiple stacked Gradient Mamba (GM) blocks, each featuring a tri-branch
structure. In addition to benefiting from efficient global feature
representation by Mamba block, we further innovatively introduce spatial
gradient attention and spectral gradient attention to guide the reconstruction
of spatial and spectral cues. GMSR-Net demonstrates a significant
accuracy-efficiency trade-off, achieving state-of-the-art performance while
markedly reducing the number of parameters and computational burdens. Compared
to existing approaches, GMSR-Net slashes parameters and FLOPS by substantial
margins of 10 times and 20 times, respectively. Code is available at
https://github.com/wxy11-27/GMSR.
