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论文类型：会议论文

发表时间：2021-04-12

页面范围：2088-2092

关键字：Image restoration; data-dependent; proximal averaged; non-convex optimization

摘要：Maximum A Posterior (MAP) acts as one of the most popular modeling scheme 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. In additionally, 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 Image Restoration (IR) tasks. Both visual and quantitative comparison results demonstrate that our method outperforms the state of the art.