M3SR用多尺度多感知机制提升高光谱重建精度与效率
M3SR: Multi-Scale Multi-Perceptual Mamba for Efficient Spectral Reconstruction
- 设计多感知融合模块，增强对高光谱特征的全面理解
- 在多个尺度上融合全局、中间与局部特征，提升重建质量
- 相比现有方法精度更高且计算开销更低，适合实际部署
Mamba架构因其出色的适应性和强大性能，被广泛应用于各类低层视觉任务。尽管已有研究将Mamba用于高光谱重建，但仍面临两大挑战：(1) 单一空间感知限制了对高光谱图像的完整理解和分析能力；(2) 单尺度特征提取难以捕捉高光谱图像中复杂的结构和精细细节。为解决上述问题，本文提出一种面向高光谱重建的多尺度、多感知Mamba架构——M3SR。具体地，设计了多感知融合模块，以增强模型对输入特征的综合理解能力。通过将该模块集成到U-Net结构中，M3SR能够有效提取并融合多尺度的全局、中间与局部特征，从而实现多尺度下的高精度重建。大量定量与定性实验表明，所提M3SR在保持较低计算成本的同时，优于现有最先进方法。
原文摘要 · Abstract (English)
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.
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