Image Restoration Via Data-Dependent Proximal Averaged Optimization

Maximum A Posterior (MAP) acts as one of the most popular modeling schemes in image restoration and is usually reduced to a separable optimization model. Unfortunately, it is challenging to establish exact regularization term and the model with complex priors is hard to optimize. On the other side, it is still hard to incorporate different domain knowledge and data-dependent information into MAP model without changing the property of the objective. To partially address the above issues, we develop a Data-dependent Proximal Averaged (DPA) paradigm through optimizing objective and data-dependent feasibility constraint for the challenging IR problems. Both visual and quantitative comparison results demonstrate that our method outperforms the state of the art.