Group Sparsity Residual Constraint Image Denoising Model with 𝒍1/𝒍2 Regularization

Abstract

Group sparse residual constraint with non-local priors (GSRC) has achieved great success in image restoration producing state-of-the-art performance. In the GSRC model, the [see formula in PDF] norm minimization is employed to reduce the group sparse residual. In recent years, non-convex regularization terms have been widely used in image denoising problems, which have achieved better results in denoising than convex regularization terms. In this paper, we use the ratio of the [see formula in PDF] and [see formula in PDF] norm instead of the [see formula in PDF] norm to propose a new image denoising model, i.e., a group sparse residual constraint model with [see formula in PDF] minimization (GSRC-[see formula in PDF]). Due to the computational difficulties arisen from the non-convexity and non-linearity, we focus on a constrained optimization problem that can be solved by alternative direction method of multipliers (ADMM). Experimental results of image denoising show that the pro-posed model outperforms several state-of-the-art image denoising methods both visually and quantitatively.