Flex-DLD: Deep Low-Rank Decomposition Model With Flexible Priors for Hyperspectral Image Denoising and Restoration

Hyperspectral images (HSIs) are composed of hundreds of contiguous waveband images, offering a wealth of spatial and spectral information. However, the practical use of HSIs is often hindered by the presence of complicated noise caused by various factors such as non-uniform sensor response and dark current. Traditional methods for denoising HSIs rely on constrained optimization approaches, where selecting appropriate prior knowledge is critical for achieving satisfactory results. Nevertheless, these traditional algorithms are limited by hand-crafted priors, leaving room for improvement in their denoising performance. Recently, the supervised deep learning technique has emerged as a promising approach for HSI denoising. However, their requirement for paired training data and poor generalization ability on untrained noise distributions pose challenges in practical applications. In this paper, we design a novel algorithm by the synergism of optimization-based methods and deep learning techniques. Specifically, we introduce a plug-and-play Deep Low-rank Decomposition (DLD) model into the optimization framework. Furthermore, we propose an effective mechanism to incorporate traditional prior knowledge into the DLD model. Finally, we provide a detailed analysis of the optimization process and convergence of the proposed method. Empirical evaluations on various tasks, including hyperspectral image denoising and spectral compressive imaging, demonstrate the superiority of our approach over state-of-the-art methods.