The Mamba architecture has been widely applied to various low-level vision tasks due to its exceptional adaptability and strong performance. Although the Mamba architecture has been adopted for spectral reconstruction, it still faces the following two challenges: (1) Single spatial perception limits the ability to fully understand and analyze hyperspectral images; (2) Single-scale feature extraction struggles to capture the complex structures and fine details present in hyperspectral images. To address these issues, we propose a multi-scale, multi-perceptual Mamba architecture for the spectral reconstruction task, called M3SR. Specifically, we design a multi-perceptual fusion block to enhance the ability of the model to comprehensively understand and analyze the input features. By integrating the multi-perceptual fusion block into a U-Net structure, M3SR can effectively extract and fuse global, intermediate, and local features, thereby enabling accurate reconstruction of hyperspectral images at multiple scales. Extensive quantitative and qualitative experiments demonstrate that the proposed M3SR outperforms existing state-of-the-art methods while incurring a lower computational cost.

翻译：Mamba架构凭借其卓越的适应性和强大的性能，已被广泛应用于各种低级视觉任务。尽管Mamba架构已被用于光谱重建，但仍面临以下两个挑战：(1) 单一空间感知限制了全面理解和分析高光谱图像的能力；(2) 单尺度特征提取难以捕捉高光谱图像中存在的复杂结构和精细细节。为解决这些问题，我们提出了一种用于光谱重建任务的多尺度、多感知Mamba架构，称为M3SR。具体而言，我们设计了一个多感知融合模块，以增强模型全面理解和分析输入特征的能力。通过将多感知融合模块集成到U-Net结构中，M3SR能够有效提取并融合全局、中间和局部特征，从而实现在多个尺度上精确重建高光谱图像。大量的定量和定性实验表明，所提出的M3SR在计算成本更低的同时，性能优于现有的最先进方法。