Wang Xinying, Huang Zhixiong, Zhang Sifan, Zhu Jiawen, Gamba Paolo, Feng Lin
School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China.
School of Information and Communication Engineering, Dalian University of Technology, Dalian, 116024, China.
Neural Netw. 2025 Aug 21;193:108020. doi: 10.1016/j.neunet.2025.108020.
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.
光谱重建的主流方法主要集中在基于卷积和基于Transformer的架构上。然而，卷积神经网络（CNN）方法在处理长距离依赖关系方面存在不足，而Transformer则受到计算效率限制的约束。因此，在确保重建高光谱图像（HSI）质量的同时构建一个高效的光谱重建网络已成为一项重大挑战。状态空间模型（如Mamba）的最新突破因其接近线性的计算效率和卓越性能，在从自然语言处理到视觉任务等领域引起了广泛关注，促使我们研究其在光谱重建问题上的潜力。为此，我们引入了用于从RGB图像进行光谱重建的梯度集成Mamba，简称为GMSR-Net。GMSR-Net是一个轻量级模型，具有全局感受野和线性计算复杂度。其核心由多个堆叠的梯度Mamba（GM）块组成，每个块都具有三分支结构。在Mamba高效的全局特征表示基础上，我们进一步创新性地提出了空间梯度注意力和光谱梯度注意力，以指导空间和光谱线索的重建。GMSR-Net展现出显著的准确性-效率权衡，在显著减少参数数量和计算负担的同时达到了当前最优性能。与现有方法相比，GMSR-Net的参数和浮点运算次数（FLOPs）分别大幅减少了8倍和20倍。代码可在https://github.com/wxy11-27/GMSR获取。
