DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

November 19, 2017 · Entered Twilight · 🏛 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Repo contents: .gitignore, LICENSE, README.md, checkpoints, data, datasets, images, models, motion_blur, options, test.py, train.py, util

Authors Orest Kupyn, Volodymyr Budzan, Mykola Mykhailych, Dmytro Mishkin, Jiri Matas arXiv ID 1711.07064 Category cs.CV: Computer Vision Citations 1.6K Venue 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Repository https://github.com/KupynOrest/DeblurGAN ⭐ 2634 Last Checked 1 month ago

Abstract

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