  Hyperspectral image (HSI) denoising is a crucial preprocessing procedure for
the subsequent HSI applications. Unfortunately, though witnessing the
development of deep learning in HSI denoising area, existing convolution-based
methods face the trade-off between computational efficiency and capability to
model non-local characteristics of HSI. In this paper, we propose a
Spatial-Spectral Transformer (SST) to alleviate this problem. To fully explore
intrinsic similarity characteristics in both spatial dimension and spectral
dimension, we conduct non-local spatial self-attention and global spectral
self-attention with Transformer architecture. The window-based spatial
self-attention focuses on the spatial similarity beyond the neighboring region.
While, spectral self-attention exploits the long-range dependencies between
highly correlative bands. Experimental results show that our proposed method
outperforms the state-of-the-art HSI denoising methods in quantitative quality
and visual results.
