Spectral Dynamic Attention Network for Hyperspectral Image Super-Resolution
Hyperspectral image super-resolution is essential for enhancing the spatial fidelity of HSI data, yet existing deep learning methods often struggle with substantial spectral redundancy and the limited non-linear modeling capacity of standard feed-forward networks (FFNs). To address these challenges, we propose Spectral Dynamic Attention Network (SDANet), a framework designed to adaptively suppress redundant spectral interactions. SDANet integrates two key components: 1) Dynamic Channel Sparse Attention (DCSA) module that computes channel-wise correlations and selectively preserves the most informative attention responses through dynamic and data-dependent sparsification. 2) Frequency-Enha...
| Authors | Tengya Zhang, Feng Gao, Lin Qi et al. |
|---|---|
| Primary category | eess.IV |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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