CALSRN: Clustering Attention-Based Low-Dimensional Spectral Reconstruction Network for Hyperspectral Image Super-Resolution

Hyperspectral image single-image super-resolution (HSI-SISR) aims to reconstruct a high-resolution hyperspectral image (HR-HSI) with high spatial resolution and high spectral fidelity from only a low-resolution observation. Existing methods still face two key challenges when processing hyperspectral images(HSIs). On the one hand, HSIs contain a large number of continuous spectral bands with severe spectral redundancy, and directly modeling features in the original highdimensional spectral space introduces heavy computational burden. On the other hand, pixels belonging to the same land-cover category may be spatially scattered while sharing similar spectral responses. Existing attention mechanisms based on fixed and rectangle windows, spectral-dimensional attention, or Top-k selection are still limited in directly modeling such pixel-level non-local self-similarity under relatively low computational complexity. To address these issues, this paper proposes a clustering attention-based low-dimensional spectral reconstruction network (CALSRN). First, a parameter-free spectral compression module is designed to compress the original high-dimensional HSI into a compact spectral representation, thereby reducing spectral redundancy and the computational cost of subsequent attention modeling. Second, a clustering attention block (CAB) is proposed. Specifically, clustering is performed on the compressed spectral HSI at the ×1 scale level to pre-generate a pixel membership matrix, which is then used to guide attention computation. In this way, spatially distant pixels with similar spectral responses can effectively exchange information. To address the difficulty of parallel computation caused by unequal cluster sizes, CAB further adopts a category-sorting-based fixed-size grouping strategy and an adjacent-group expansion strategy. Meanwhile, pixel-to-category-center inter-group cross-attention is introduced to incorporate global category-level contextual information. Third, hierarchical reconstruction loss and dynamic weight balancing are introduced to supervise intermediate reconstruction results and stabilize progressive multi-scale training. Experimental results demonstrate that the proposed CALSRN effectively improves spatial detail recovery and spectral fidelity for hyperspectral image super-resolution.