Enhanced Non-Local Cascading Network With Attention Mechanism For Hyperspectral Image Denoising

Because of the complexity of imaging environment, hyperspectral remote sensing images (HSIs) often suffer from different kinds of noise. Despite the success in natural image denoising, most of the existing CNN-based HSIs denoising methods still suffer from the problem of inadequate noise suppression and insufficient feature extraction. In this paper, a novel HSIs denoising algorithm based on an enhanced nonlocal cascading network with attention mechanism (ENCAM) is proposed, which can extract the joint spatial-spectral feature more effectively. The main contributions include: (1) the non-local structure is introduced to enlarge the receptive field to extract the spatial features more effectively; (2) multi-scale convolutions and channel attention module are applied to enhance extracted multi-scale features; (3) a cascading residual dense structure is used to extract different frequency features. Both of the theoretical analysis and the experiments indicate that the proposed method is superior to the other state-of-theart methods on HSIs denoising.