Context-Aware Guided Attention Based Cross-Feedback Dense Network for Hyperspectral Image Super-Resolution
Wenqian Dong, Jiahui Qu, Tongzhen Zhang, Yunsong Li, Qian Du
Abstract
Convolutional neural networks (CNNs) have shown impressive performance in computer vision due to their non-linearity. Particularly, DenseNet that facilitates feature re-use in a feedforward manner has achieved state-of-the-art reconstruction accuracy for super-resolution (SR). However, most DenseNet based SR models transfer the features generated from each layer to all the subsequent layers, inevitably introducing redundancy, especially for high-dimensional hyperspectral (HS) images. To tackle this problem, we propose a two-branch cross-feedback dense network with context-aware guided attention (CFDcagaNet) for HS super-resolution (HSSR), which allows the network to learn the attention maps of high-level features and refine the low-level features in a feedback manner across two branches. Context-aware guided attention uses high-level posterior information to provide more faithful spatial-spectral guidance for low-level features, which enables CFDcagaNet to learn more effective spatial-spectral features at low levels and yield more effective spatial-spectral transfer in the network. Extensive experiments on widely-used datasets demonstrate that the proposed method outperforms state-of-the-art methods in terms of both quantitative values and visual qualities.