Hyperspectral Image Denoising via Correntropy-Based Nonconvex Low-Rank Approximation

Hyperspectral images (HSIs) are prone to be corrupted by various types of noise during the process of imaging and transmission, which seriously affect the subsequent HSI processing tasks. In this article, we proposed a novel low-rank-based model for HSIs denoising. On one hand, motivated by the superiority of nonconvex approximation to matrix rank, we construct a new nonconvex function which is tighter than some of existing rank approximated functions. On the other hand, we describe Gaussian noise and sparse noise simultaneously by introducing correntropy. In comparison with traditional model, we constrain noise in one regularization instead of separately constraining Gaussian noise and sparse noise, which indicates that the number of regularization parameters have been reduced. To optimize the proposed model, some convex analysis tools are utilized in this article. In addition, we provide theoretical analysis on the convergence of the developed algorithm. Through experiments conducted on both simulated and real HSIs, we verify the superiority of our model in enhancing the performance of mixture noise removal in HSIs.