Volodymyr Budzan

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We present an end-to-end learning approach for motion deblurring, which is based on conditional GAN and content loss. It improves the state-of-the art in terms of peak signalto-noise ratio, 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(More)
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