A fast and effective motion deblurring method has great application values in real life. This work presents an innovative approach in which a self-paced learning is combined with GAN to deblur image. First, We explain that a proper generator can be used as deep priors and point out that the solution for pixel-based loss is not same with the one for perception-based loss. By using these ideas as starting points, a Bi-Skip network is proposed to improve the generating ability and a bi-level loss is adopted to solve the problem that common conditions are non-identical. Second, considering that the complex motion blur will perturb the network in the training process, a self-paced mechanism is adopted to enhance the robustness of the network. Through extensive evaluations on both qualitative and quantitative criteria, it is demonstrated that our approach has a competitive advantage over state-of-the-art methods.
@article{arxiv.1902.08915,
title = {Bi-Skip: A Motion Deblurring Network Using Self-paced Learning},
author = {Yiwei Zhang and Chunbiao Zhu and Ge Li and Yuan Zhao and Haifeng Shen},
journal= {arXiv preprint arXiv:1902.08915},
year = {2019}
}