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.

翻译：主流光谱重建方法主要聚焦于设计基于卷积和Transformer的架构。然而，CNN方法在处理长距离依赖时面临挑战，而Transformer则受限于计算效率的瓶颈。近期状态空间模型（如Mamba）的突破性进展，凭借其近线性的计算效率与优异性能引起了广泛关注，促使我们探索其在光谱重建问题中的潜力。为此，我们提出了面向RGB图像光谱重建的梯度引导Mamba网络（GMSR-Net）。GMSR-Net是一种具有全局感受野与线性计算复杂度的轻量级模型。其核心由多个堆叠的梯度Mamba（GM）模块构成，每个模块采用三分支结构。除了通过Mamba模块获得高效的全局特征表征外，我们创新性地引入空间梯度注意力与光谱梯度注意力来指导空间与光谱线索的重建。GMSR-Net在精度与效率之间取得了显著平衡，在参数规模与计算负载大幅缩减的同时实现了最先进的性能。相比现有方法，GMSR-Net的参数数量与FLOPS分别降低了10倍与20倍。代码已开源至https://github.com/wxy11-27/GMSR。