Conditional Adversarial Networks for Blind Image Deblurring

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dc.contributor.author Kupyn, Orest
dc.date.accessioned 2018-01-23T23:55:46Z
dc.date.available 2018-01-23T23:55:46Z
dc.date.issued 2018
dc.identifier.citation Kupyn, Orest. Conditional Adversarial Networks for Blind Image Deblurring : Master Thesis : manuscript rights / Orest Kupyn ; Supervisor Dr. Rostyslav Hryniv ; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2017. – 44 p. : ill. uk
dc.identifier.uri http://er.ucu.edu.ua/handle/1/1189
dc.description.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. uk
dc.language.iso en uk
dc.subject Conditional Adversarial Networks uk
dc.subject DeblurGAN uk
dc.title Conditional Adversarial Networks for Blind Image Deblurring uk
dc.type Preprint uk
dc.status Публікується вперше uk


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