LAUSR

Convolutional neural networks (CNNs) have shown impressive performance in computer vision due to their nonlinearity. Particularly, DenseNet (DN) that facilitates feature reuse in a feedforward (FF) manner has achieved state-of-the-art reconstruction accuracy for super-resolution (SR). However, most DN-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 (FB) manner across two branches. Context-aware guided attention (CAGA) 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.