GMSR: Gradient-integrated mamba for spectral reconstruction from RGB images.

Mainstream approaches to spectral reconstruction primarily focus on Convolution- and Transformer-based architectures. However, CNN methods fall short in handling long-range dependencies, whereas Transformers are constrained by computational efficiency limitations. Therefore, constructing a efficient spectral reconstruction network while ensuring the quality of reconstructed hyperspectral images (HSIs) has become a major challenge. Recent breakthroughs in the state-space model (e.g., Mamba) have attracted significant attention from natural language processing to vision tasks due to its near-linear computational efficiency and superior performance, prompting our investigation into its potential for spectral reconstruction problems. To this end, we introduce the Gradient-integrated 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. Building upon the efficient global feature representation from the Mamba, we further innovatively propose 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 8 times and 20 times, respectively. Code is available at https://github.com/wxy11-27/GMSR.