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DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
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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 -- DeepDeblur. 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
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Cited by 1 Pith paper
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Blind Image Deconvolution using Pretrained Generative Priors
Blind deconvolution is solved by alternating gradient descent in the latent spaces of pretrained image and blur-kernel generators, with a slack variant that relaxes the image constraint.
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