Multilevel Progressive Network With Nonlocal Channel Attention for Hyperspectral Image Super-Resolution

Deep convolutional neural networks (CNNs) have made great progress in the super-resolution (SR) of hyperspectral images (HSIs). However, most methods utilize convolution to explore local features, and global features are ignored. It is expected that combining nonlocal mechanism with CNN will improve the performance of HSI SR. This article presents a multilevel progressive HSI SR network. The dense nonlocal and local block (DNLB) is constructed to combine local and global features, which are used to reconstruct SR images at each level. Due to the high dimension of HSI, original nonlocal methods produce memory-expensive attention maps. We develop a nonlocal channel attention block to extract the global features of HSIs efficiently. Spatial-spectral gradient is injected in the nonlocal attention block to obtain better details. Furthermore, the progressive learning mode-based multilevel network is proposed to reconstruct HSI with fine details. A number of experiments demonstrate that our method can reconstruct HSIs more accurately than existing methods.