A Non-Convex Hybrid Overlapping Group Sparsity Model with Hyper-Laplacian Prior for Multiplicative Noise

Multiplicative noise removal is a quite challenging problem in image denoising. In recent years, hyper-Laplacian prior information has been successfully introduced the denoising and significant effects have achieved. this paper, we propose new hybrid regularizer model for removing multiplicative noise. The proposed consists of non-convex higher-order total variation overlapping group sparsity on regularizer. It combines advantages regularization regularization, which may simultaneously preserve fine-edge reduce staircase effect at same time. We develop an effective alternating minimization method nonconvex via direction multipliers framework, where majorization–minimization algorithm iteratively reweighted are adopted to solve corresponding subproblems. Numerical experiments show that outperforms most advanced terms visual quality certain measurements.