DeblurGAN: Blind motion deblurring using conditional adversarial networks

O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, J. Matas. DeblurGAN: Blind motion deblurring using conditional adversarial networks. pages 8183-8192, DOI 10.1109/CVPR.2018.00854, 12, 2018.

Autoren
  • Orest Kupyn
  • Volodymyr Budzan
  • Mykola Mykhailych
  • Dmytro Mishkin
  • Jiri Matas
BuchProceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018)
TypKonferenzband
DOI10.1109/CVPR.2018.00854
ISBN978-1-5386-6420-9
Monat12
Jahr2018
Seiten8183-8192
Abstract

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.