Restoration of Poissonian Images Using Nonconvex Regularizer with Overlapping Group Sparsity

Aimed at achieving the accurate restoration of Poissonian images that exhibit neat edges and no staircase effect, this article develops a novel hybrid nonconvex double regularizer model. The proposed scheme closely takes advantages total variation with overlapping group sparsity high-order priors. is adopted to globally suppress artifacts, while regularization plays role locally preserving significant image features edge details. Computationally, quite efficient alternating direction method multipliers, associated iteratively reweighted ${\ell _{1}}$ algorithm majorization-minimization method, employed settle optimization problem iteratively. Finally, exhaustive simulation experiments are executed for recovering images, which made comparisons several state-of-the-art strategies, indicate brilliant performance our model in terms intuitive effects accuracy evaluation.