AI 中文总结
本文针对现有Mamba模型用于高光谱图像超分辨率时破坏空间邻接性、未对齐图像固有特征的问题，提出USP-Mamba框架，通过解混光谱提示、结构提示及互补扫描，在多数据集上性能优于现有方法。
AI 中文摘要
高光谱图像超分辨率旨在重建高分辨率图像，同时保留密集的光谱信息。近来，基于Mamba的模型凭借线性计算复杂度捕获长程依赖关系，在该任务中展现出良好潜力。然而，其因果序列建模要求将二维高光谱特征沿预设扫描顺序展开，这会破坏空间邻接性，限制上下文信息的有效传播；此外，现有模型的状态空间参数化主要源自通用学习表示，未与高光谱图像的固有特征明确对齐。为解决这一问题，本文提出一种基于解混的光谱与结构提示的Mamba框架，即USP-Mamba，它通过成分感知的光谱先验和图像依赖的结构提示来调整Mamba状态演化。具体而言，解混驱动的光谱提示捕获输入图像的全局材料成分，并在整个重建过程中提供持续的条件约束；该提示被注入Mamba序列并跨层逐步调整，引导状态演化实现成分一致的重建。本文引入包含空间和频率分量的特征级结构提示，提供图像依赖的局部指导：空间提示促进对局部细节保留敏感的结构感知状态编码，频率提示实现同质区域与高频细节间的区域自适应过渡。最后，互补的希尔伯特扫描和语义引导邻域扫描可保留空间连续性，强化非局部语义依赖建模。在不同数据集上的大量实验表明，所提方法始终优于代表性方法。
英文摘要
Hyperspectral image super-resolution aims to reconstruct high-resolution imagery while preserving dense spectral information. Recently, Mamba-based models have shown promising potential for this task by capturing long-range dependencies with linear computational complexity. Nevertheless, their causal sequence modeling requires two-dimensional hyperspectral features to be unfolded along predefined scanning orders, which disrupts spatial adjacency and restricts the effective propagation of contextual information. Moreover, state-space parameterization of existing models is predominantly derived from generic learned representations, without explicit alignment with the intrinsic characteristics of the hyperspectral image. To address this issue, we propose an Unmixing-derived Spectral and Structural Prompting Mamba framework, termed USP-Mamba, which adapts Mamba state evolution through composition-aware spectral priors and image-dependent structural prompts. Specifically, an unmixing-informed spectral prompt captures the global material composition of the input image and provides persistent conditioning throughout reconstruction. Injected into the Mamba sequence and progressively adapted across layers, it steers state evolution toward composition-consistent reconstruction. We introduce feature-level structural prompts comprising spatial and frequency components to provide image-dependent local guidance. The spatial prompt promotes structure-sensitive state encoding for local detail preservation, while the frequency prompt enables region-adaptive transitions between homogeneous regions and high-frequency details. Finally, complementary Hilbert and Semantic-Guided Neighboring scans preserve spatial continuity and strengthen non-local semantic dependency modeling. Extensive experiments on different datasets demonstrate that the proposed method consistently outperforms representative approaches.
