In this paper, we propose an end-to-end learning framework for event-based motion deblurring in a self-supervised manner, where real-world events are exploited to alleviate the performance degradation caused by data inconsistency. To achieve this end, optical flows are predicted from events, with which the blurry consistency and photometric consistency are exploited to enable self-supervision on the deblurring network with real-world data. Furthermore, a piece-wise linear motion model is proposed to take into account motion non-linearities and thus leads to an accurate model for the physical formation of motion blurs in the real-world scenario. Extensive evaluation on both synthetic and real motion blur datasets demonstrates that the proposed algorithm bridges the gap between simulated and real-world motion blurs and shows remarkable performance for event-based motion deblurring in real-world scenarios.
@article{arxiv.2109.13695,
title = {Motion Deblurring with Real Events},
author = {Fang Xu and Lei Yu and Bishan Wang and Wen Yang and Gui-Song Xia and Xu Jia and Zhendong Qiao and Jianzhuang Liu},
journal= {arXiv preprint arXiv:2109.13695},
year = {2021}
}