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Deep learning (DL) based hyperspectral images (HSIs) denoising approachesdirectly learn the nonlinear mapping between observed noisy images andunderlying clean images. They normally do not consider the physicalcharacteristics of HSIs, therefore making them lack of interpretability that iskey to understand their denoising mechanism.. In order to tackle this problem,we introduce a novel model guided interpretable network for HSI denoising.Specifically, fully considering the spatial redundancy, spectral low-ranknessand spectral-spatial properties of HSIs, we first establish a subspace basedmulti-dimensional sparse model. This model first projects the observed HSIsinto a low-dimensional orthogonal subspace, and then represents the projectedimage with a multidimensional dictionary. After that, the model is unfoldedinto an end-to-end network named SMDS-Net whose fundamental modules areseamlessly connected with the denoising procedure and optimization of themodel. This makes SMDS-Net convey clear physical meanings, i.e., learning thelow-rankness and sparsity of HSIs. Finally, all key variables includingdictionaries and thresholding parameters are obtained by the end-to-endtraining. Extensive experiments and comprehensive analysis confirm thedenoising ability and interpretability of our method against thestate-of-the-art HSI denoising methods.

SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising