Spectral Dynamic Attention Network for Hyperspectral Image Super-Resolution

• URL: http://arxiv.org/abs/2604.27326v1
• Date: Thu, 30 Apr 2026 02:13:33 GMT
• Title: Spectral Dynamic Attention Network for Hyperspectral Image Super-Resolution
• Abstract summary: Spectral Dynamic Attention Network (SDANet) is a framework designed to adaptively suppress redundant spectral interactions.<n>It achieves state-of-the-art HISR performance while maintaining competitive efficiency.
• Score: 45.72534786125302
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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-Enhanced Feed-Forward Network (FE-FFN) that jointly models spatial and frequency-domain representations to enhance non-linear expressiveness. Extensive experiments on two benchmark datasets demonstrate that SDANet achieves state-of-the-art HISR performance while maintaining competitive efficiency. The code will be made publicly available at https://github.com/oucailab/SDANet.

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