Non-Local Regularized Variational Model for Image Deblurring under Mixed Gaussian-Impulse Noise

Abstract

The presence of mixed Gaussian-impulse noise makes image deblurring much more challenging in real-world applications. To guarantee high-quality deblurred images, a non-local regularized variational model is proposed in this paper for restoration of blurred images corrupted by mixed Gaussian-impulse noise. The proposed image restoration model is mainly composed of a mixed L^(1,2) data-fidelity term and a non-local total variation regularizer. The nonlocal regularizer is capable of suppressing staircase-like artifacts and preserving fine structures since it takes full advantage of high degree of self-similarity and redundancy within images. However, the mixed data-fidelity term and non-local regularizer make the image deblurring problem arithmetically difficult to solve. To guarantee solution stability and efficiency, an alternating minimization algorithm is developed to solve the resulting optimization problem. In particular, the original optimization problem can be decomposed into two simpler subproblems. Each subproblem is then efficiently solved using existing numerical method. Comprehensive experiments on grayscale and color test images have been carried out to compare our proposed method with several state-of the- art image restoration methods. Experimental results demonstrated that our proposed method was more capable of removing both blurring effect and mixed Gaussian-impulse noise, as well as preserving fine image details.