HYPERSPECTRAL IMAGE DENOISING USING LOW-RANK AND SPARSE MODEL BASED DEEP UNROLLING

Hyperspectral image (HSI) denoising methods that are implemented using deep learning frameworks rarely consider the intrinsic characteristics of HSIs, and often lack both physical interpretability, and generalization. In this paper, a lowrank and sparse model-based unrolled network for HSI denoising, termed LRS-Net, is proposed. The method unrolls a model-based denoising method into a deep-unrolled network. The network is much faster than the previous method and is also able to automatically select the tuning parameters. The method inherits the advantages of model-based methods, i.e., physical interpretability and generalization, and also advantages from deep learning based methods, i.e., computational efficiency and data-based learning capabilities. Using both simulated and real HSIs it is shown the proposed method can outperform other comparative methods, both in quantitative and visual assessments.