  Denoising is a crucial step for hyperspectral image (HSI) applications.
Though witnessing the great power of deep learning, existing HSI denoising
methods suffer from limitations in capturing the non-local self-similarity.
Transformers have shown potential in capturing long-range dependencies, but few
attempts have been made with specifically designed Transformer to model the
spatial and spectral correlation in HSIs. In this paper, we address these
issues by proposing a spectral enhanced rectangle Transformer, driving it to
explore the non-local spatial similarity and global spectral low-rank property
of HSIs. For the former, we exploit the rectangle self-attention horizontally
and vertically to capture the non-local similarity in the spatial domain. For
the latter, we design a spectral enhancement module that is capable of
extracting global underlying low-rank property of spatial-spectral cubes to
suppress noise, while enabling the interactions among non-overlapping spatial
rectangles. Extensive experiments have been conducted on both synthetic noisy
HSIs and real noisy HSIs, showing the effectiveness of our proposed method in
terms of both objective metric and subjective visual quality. The code is
available at https://github.com/MyuLi/SERT.
