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 -Deep-Deblur [25] . 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
Moreover, Kupyn et al. REF proposed an endto-end learned method for deblurring based on conditional generative adversarial networks (GAN).
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Computer Science