Image Restoration Via Data-Dependent Proximal Averaged Optimization
Pan Mu, Jian Chen, Risheng Liu, Wei Zhong, Xin Fan, Zhongxuan Luo
Abstract
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.
BibTeX
@inproceedings{icassp2020_imagerestoration,
title = {Image Restoration Via Data-Dependent Proximal Averaged Optimization},
author = {Pan Mu and Jian Chen and Risheng Liu and Wei Zhong and Xin Fan and Zhongxuan Luo},
booktitle = {ICASSP 2020},
year = {2020}
}