  Hyperspectral imaging (HI) has emerged as a powerful tool in diverse fields
such as medical diagnosis, industrial inspection, and agriculture, owing to its
ability to detect subtle differences in physical properties through high
spectral resolution. However, hyperspectral images (HSIs) are often quite noisy
because of narrow band spectral filtering. To reduce the noise in HSI data
cubes, both model-driven and learning-based denoising algorithms have been
proposed. However, model-based approaches rely on hand-crafted priors and
hyperparameters, while learning-based methods are incapable of estimating the
inherent degradation patterns and noise distributions in the imaging procedure,
which could inform supervised learning. Secondly, learning-based algorithms
predominantly rely on CNN and fail to capture long-range dependencies,
resulting in limited interpretability. This paper proposes a
Degradation-Noise-Aware Unfolding Network (DNA-Net) that addresses these
issues. Firstly, DNA-Net models sparse noise, Gaussian noise, and explicitly
represent image prior using transformer. Then the model is unfolded into an
end-to-end network, the hyperparameters within the model are estimated from the
noisy HSI and degradation model and utilizes them to control each iteration.
Additionally, we introduce a novel U-Shaped Local-Non-local-Spectral
Transformer (U-LNSA) that captures spectral correlation, local contents, and
non-local dependencies simultaneously. By integrating U-LNSA into DNA-Net, we
present the first Transformer-based deep unfolding HSI denoising method.
Experimental results show that DNA-Net outperforms state-of-the-art methods,
and the modeling of noise distributions helps in cases with heavy noise.
