Conditional Adversarial Networks for Blind Image Deblurring

Abstract

We present an end-to-end learning approach for motion deblurring, which is based on conditional GAN and content loss – DeblurGAN. DeblurGAN achieves state-of-the art in structural similarity measure and by 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. Second, we present a novel method of generating synthetic motion blurred images from the sharp ones, which allows realistic dataset augmentation.