We present DeblurGAN, an end-to-end learned method for motion deblurring. The learning is based on a conditional GAN and the content loss . DeblurGAN achieves state-of-the art performance both in the structural similarity measure and visual appearance. The quality of the deblurring model is also evaluated in a novel way on a real-world problem -- object detection on (de-)blurred images. The method is 5 times faster than the closest competitor -- DeepDeblur. We also introduce a novel method for generating synthetic motion blurred images from sharp ones, allowing realistic dataset augmentation. The model, code and the dataset are available at https://github.com/KupynOrest/DeblurGAN
@article{arxiv.1711.07064,
title = {DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks},
author = {Orest Kupyn and Volodymyr Budzan and Mykola Mykhailych and Dmytro Mishkin and Jiri Matas},
journal= {arXiv preprint arXiv:1711.07064},
year = {2018}
}