Hyperspectral Image Restoration using Nonconvex Hybrid Regularization

Abstract

Hyperspectral image (HSI) restoration is an essential pre-processing step in order to obtain more useful images for subsequent applications. However, traditional methods based on convex regularization or nonconvex spectral penalty alone are not able to fully exploit the spatial-spectral properties of the HSI datasets. In this paper, by utilizing the nonconvex spectral penalty and the nonconvex spatial penalty, we propose a novel nonconvex hybrid regularization (NHR) model, which can preserve the image features and remove the mixed noise, including Gaussian noise, stripes, deadlines, and etc. The corresponding optimization problem can be efficiently solved using an iterative algorithm based on the Augmented Lagrangian Multipliers (ALM) method. Experimental results on both simulated and real HSI images prove that the proposed NHR method significantly improves the image quality.