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Information × Registration Number 2118U002684, Article popup.category Препринт Title popup.author Kupyn Orest popup.publication 01-01-2018 popup.source_user Український католицький університет popup.source https://hdl.handle.net/20.500.14570/1189 popup.publisher Description 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. popup.nrat_date 2025-05-09 Close