HSRMamba：面向单幅图像高光谱超分辨率的上下文空-谱状态空间模型
HSRMamba: Contextual Spatial-Spectral State Space Model for Single Image Hyperspectral Super-Resolution
AI总结：
针对Mamba应用于高光谱图像超分辨率时忽略局部空-谱关联、对输入顺序敏感的问题，提出HSRMamba模型，通过局部空-谱划分与全局光谱重排序优化，在量化指标和视觉效果上均超越现有最优方法。
摘要：
Mamba凭借强大的全局建模能力和线性计算复杂度，在视觉任务中展现出优异性能，为高光谱图像超分辨率（HSISR）带来了可观的应用潜力。但在HSISR任务中，Mamba存在诸多挑战：将图像转换为一维序列的做法忽略了局部相邻像素间的空-谱结构关联，且其性能对输入顺序高度敏感，会影响空间细节与光谱细节的恢复。本文提出HSRMamba——一种面向HSISR的上下文空-谱建模状态空间模型，从局部和全局两个层面解决上述问题。具体而言，设计了局部空-谱划分机制，在三维特征的相邻像素间建立块级因果关系，缓解局部遗忘问题；此外，采用基于光谱相似性的全局光谱重排序策略，增强空间和光谱维度上相似像素的因果表征。最后，实验结果表明，HSRMamba在量化质量和视觉效果上均优于现有最优方法，代码可在https://github.com/Tomchenshi/HSRMamba获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
