Papers › DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
Orest Kupyn, Volodymyr Budzan, Mykola Mykhailych, Dmytro Mishkin, Jiri Matas
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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Code
13 repositories listed; official and paper-mentioned ones first.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Deblurring | REDS | DeblurGAN | Average PSNR | 24.09 | #3 of 3 | Archive leaderboard | report |
| Deblurring | RealBlur-J (trained on GoPro) | DeblurGAN | SSIM (sRGB) | 0.834 | #15 of 15 | Archive leaderboard | report |
| Deblurring | RealBlur-R (trained on GoPro) | DeblurGAN | SSIM (sRGB) | 0.903 | #18 of 19 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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