Hyperspectral Image Denoising With Weighted Nonlocal Low-Rank Model and Adaptive Total Variation Regularization

Hyperspectral image (HSI) is always corrupted by various types of noise during image capturing, such as Gaussian noise, stripe noise, deadline noise, impulse noise, and more. Such complicated noise significantly degrades imaging quality and thus limits the performance of downstream vision tasks. Current HSI denoising methods tackle this problem by modeling either the spectral-spatial prior of HSI or the noise characteristic of HSI, and few work consider the two aspects simultaneously. In this paper, we propose a new HSI denoising method by simultaneously modeling the HSI prior and the HSI noise characteristic. Specifically, we firstly utilize the non independent and identically distributed (non i.i.d.) mixture of Gaussian (MoG) assumption to characterize the complex noise, which corresponds to optimize a weighted fidelity function. Secondly, we exploit HSI's non-local similarity and spatial-spectral correlation priors by applying non-local low rank model. Thirdly, we design an adaptive edge preserving total variation regularization term to characterize the non-local smooth property of HSI. Finally, we propose a new denoising model and develop effective ADMM algorithm to solve it. Extensive experiments on simulated data and real data substantiate the superiority of the proposed method beyond state-of-the-arts.