  Hyperspectral images (HSIs) often suffer from noise arising from both
intra-imaging mechanisms and environmental factors. Leveraging domain knowledge
specific to HSIs, such as global spectral correlation (GSC) and non-local
spatial self-similarity (NSS), is crucial for effective denoising. Existing
methods tend to independently utilize each of these knowledge components with
multiple blocks, overlooking the inherent 3D nature of HSIs where domain
knowledge is strongly interlinked, resulting in suboptimal performance. To
address this challenge, this paper introduces a spatial-spectral recurrent
transformer U-Net (SSRT-UNet) for HSI denoising. The proposed SSRT-UNet
integrates NSS and GSC properties within a single SSRT block. This block
consists of a spatial branch and a spectral branch. The spectral branch employs
a combination of transformer and recurrent neural network to perform recurrent
computations across bands, allowing for GSC exploitation beyond a fixed number
of bands. Concurrently, the spatial branch encodes NSS for each band by sharing
keys and values with the spectral branch under the guidance of GSC. This
interaction between the two branches enables the joint utilization of NSS and
GSC, avoiding their independent treatment. Experimental results demonstrate
that our method outperforms several alternative approaches. The source code
will be available at https://github.com/lronkitty/SSRT.
