Nonlocal Spatial–Spectral Neural Network for Hyperspectral Image Denoising

Hyperspectral image (HSI) denoising is an essential preprocessing step to improve the quality of HSIs. The difficulty of HSI denoising lies in effectively modeling the intrinsic characteristics of HSIs, such as spatial–spectral correlation (SSC), global spectral correlation (GSC), and nonlocal spatial correlation. This article introduces a nonlocal spatial–spectral neural network (NSSNN) for HSI denoising by considering the above three factors in a unified network. More specifically, NSSNN is based on the residual U-Net and embedded with the introduced spatial–spectral recurrent (SSR) blocks and nonlocal self-similarity (NSS) blocks. The SSR block comprises 3-D convolutions, one light recurrence, and one highway network. 3-D convolution helps exploit the SSC. The light recurrence and highway network make up the recurrent computation component and refined component, respectively, to model the GSC. The NSS block is based on crisscross attention and can exploit long-range spatial contexts effectively and efficiently. Attributing to effective modeling of the SSC, the GSC, and the nonlocal spatial correlation, our NSSNN has a strong denoising ability. Extensive experiments show the superior denoising effectiveness of our method on synthetic and real-world datasets compared to alternative methods. The source code will be available at https://github.com/lronkitty/NSSNN.