提出HSRMamba模型，提升高光谱图像超分辨率的细节恢复能力。
HSRMamba: Contextual Spatial-Spectral State Space Model for Single Image Hyperspectral Super-Resolution
- 设计局部时空分区机制，保留邻近像素的结构关系
- 通过光谱相似性重排，增强跨空间光谱的因果建模
- 在定量与视觉效果上均超越当前最优方法
Mamba凭借强大的全局建模能力和线性计算复杂度，在视觉任务中表现卓越，为高光谱图像超分辨率（HSISR）带来巨大潜力。然而，在HSISR中，将图像转为一维序列会忽略局部邻近像素间的时空结构关系，且对输入顺序敏感，影响空间与光谱细节的重建。为此，本文提出HSRMamba，一种面向HSISR的上下文时空-光谱状态空间模型，从局部与全局两方面解决上述问题。具体地，设计了局部时空分区机制，建立3D特征中相邻像素间的分块因果关系，缓解局部遗忘问题；同时，提出基于光谱相似性的全局光谱重排策略，增强跨空间与光谱维度下相似像素的因果表示。实验结果表明，所提方法在定量指标与视觉效果上均优于现有最先进方法。代码已开源：https://github.com/Tomchenshi/HSRMamba。
原文摘要 · Abstract (English)
Mamba has demonstrated exceptional performance in visual tasks due to its powerful global modeling capabilities and linear computational complexity, offering considerable potential in hyperspectral image super-resolution (HSISR). However, in HSISR, Mamba faces challenges as transforming images into 1D sequences neglects the spatial-spectral structural relationships between locally adjacent pixels, and its performance is highly sensitive to input order, which affects the restoration of both spatial and spectral details. In this paper, we propose HSRMamba, a contextual spatial-spectral modeling state space model for HSISR, to address these issues both locally and globally. Specifically, a local spatial-spectral partitioning mechanism is designed to establish patch-wise causal relationships among adjacent pixels in 3D features, mitigating the local forgetting issue. Furthermore, a global spectral reordering strategy based on spectral similarity is employed to enhance the causal representation of similar pixels across both spatial and spectral dimensions. Finally, experimental results demonstrate our HSRMamba outperforms the state-of-the-art methods in quantitative quality and visual results. Code is available at: https://github.com/Tomchenshi/HSRMamba.
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