In this paper, we propose a new control framework called the moving endpoint control to restore images corrupted by different degradation levels in one model. The proposed control problem contains a restoration dynamics which is modeled by an RNN. The moving endpoint, which is essentially the terminal time of the associated dynamics, is determined by a policy network. We call the proposed model the dynamically unfolding recurrent restorer (DURR). Numerical experiments show that DURR is able to achieve state-of-the-art performances on blind image denoising and JPEG image deblocking. Furthermore, DURR can well generalize to images with higher degradation levels that are not included in the training stage.
@article{arxiv.1805.07709,
title = {Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration},
author = {Xiaoshuai Zhang and Yiping Lu and Jiaying Liu and Bin Dong},
journal= {arXiv preprint arXiv:1805.07709},
year = {2018}
}