通过动态关注关键波段，提升高光谱图像超分辨率质量
Spectral Dynamic Attention Network for Hyperspectral Image Super-Resolution
- 用动态稀疏注意力机制筛选重要波段，抑制冗余信息
- 融合空间与频域特征，增强模型非线性表达能力
- 在两个基准数据集上达到顶尖效果，适合遥感图像处理研究者
高光谱图像超分辨率对提升HSI数据的空间保真度至关重要，但现有深度学习方法常受限于严重的光谱冗余以及标准前馈网络（FFNs）的非线性建模能力不足。为此，我们提出谱动态注意力网络（SDANet），一种自适应抑制冗余光谱交互的框架。SDANet集成两个核心组件：1）动态通道稀疏注意力（DCSA）模块，通过计算通道间相关性并基于动态、数据依赖的稀疏化选择保留最具信息量的注意力响应；2）频域增强前馈网络（FE-FFN），联合建模空间与频域表征，提升非线性表达能力。在两个基准数据集上的大量实验表明，SDANet在保持高效的同时实现了领先的高光谱图像超分辨率性能。代码将公开于 https://github.com/oucailab/SDANet。
原文摘要 · Abstract (English)
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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