A Denoising Network Based on Frequency-Spectral- Spatial-Feature for Hyperspectral Image

The quality of hyperspectral images seriously impedes subsequent high-level vision tasks such as image segmentation, image encoding, and target detection. However, the frequency, spectral, and spatial properties of the hyperspectral noise pictures are not utilized fully by existing image denoising algorithms. To address this issue, a novel convolutional network based on united Octave and attention mechanism (UOANet) is proposed to extract the frequency-spectral-spatial-feature for denoising the actual noise of HSIs. In particular, the negative residual mapping embedded in Unet is proposed for multiscale abstract representation and two modules are designed for modeling global noisy HSI features in the frequency-spectral-spatial domain. First, with the use of residual Octave convolution module, our model can focus on the intrinsic properties of HSI noise distribution for desirable noise removal. Next, a parallel spatial-spectral attention module is used to fully utilize the rich spectrum data and the various spatial data of each band in HSI, which improves the richness of HSI details after denoising. Experimental results on both synthetic and real HSIs demonstrate the validity and superiority of UOANet compared with the state-of-the-arts under various noise settings.