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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Image Deblurring using Generative Adversarial Networks
| Date | Stars |
|---|---|
| 2026-07-24 | 2636 |
| 2026-07-25 | 2636 |
| 2026-07-28 | 2638 |
| 2026-07-30 | 2638 |
| 2026-08-06 | 2638 |
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# DeblurGAN
[arXiv Paper Version](https://arxiv.org/pdf/1711.07064.pdf)
Pytorch implementation of the paper DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks.
Our network takes blurry image as an input and procude the corresponding sharp estimate, as in the example:
<img src="images/animation3.gif" width="400px"/> <img src="images/animation4.gif" width="400px"/>
The model we use is Conditional Wasserstein GAN with Gradient Penalty + Perceptual loss based on VGG-19 activations. Such architecture also gives good results on other image-to-image translation problems (super resolution, colorization, inpainting, dehazing etc.)
## How to run
### Prerequisites
- NVIDIA GPU + CUDA CuDNN (CPU untested, feedback appreciated)
- Pytorch
Download weights from [Google Drive](https://drive.google.com/file/d/1liKzdjMRHZ-i5MWhC72EL7UZLNPj5_8Y/view?usp=sharing) . Note that during the inference you need to keep only Generator weights.
Put the weights into
```bash
/.checkpoints/experiment_name
```
To test a model put your blurry images into a folder and run:
```bash
python test.py --dataroot /.path_to_your_data --model test --dataset_mode single --learn_residual
```
## Data
Download dataset for Object Detection benchmark from [Google Drive](https://drive.google.com/file/d/1CPMBmRj-jBDO2ax4CxkBs9iczIFrs8VA/view?usp=sharing)
## Train
If you want to train the model on your data run the following command to create image pairs:
```bash
python datasets/combine_A_and_B.py --fold_A /path/to/data/A --fold_B /path/to/data/B --fold_AB /path/to/data
```
And then the following command to train the model
```bash
python train.py --dataroot /.path_to_your_data --learn_residual --resize_or_crop crop --fineSize CROP_SIZE (we used 256)
```
## Other Implementations
[Keras Blog](https://blog.sicara.com/keras-generative-adversarial-networks-image-deblurring-45e3ab6977b5)
[Keras Repository](https://github.com/RaphaelMeudec/deblur-gan)
## Citation
If you find our code helpful in your research or work please cite our paper.
```
@article{DeblurGAN,
title = {DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks},
author = {Kupyn, Orest and Budzan, Volodymyr and Mykhailych, Mykola and Mishkin, Dmytro and Matas, Jiri},
journal = {ArXiv e-prints},
eprint = {1711.07064},
year = 2017
}
```
## Acknowledgments
Code borrows heavily from [pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix). The images were taken from GoPRO test dataset - [DeepDeblur](https://github.com/SeungjunNah/DeepDeblur_release)
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Orest Kupyn · United Kingdom
46
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vugia truong · Japan
1
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8d10f6f730f8ccde, topic:deep-learning, topic:neural-network, topic:pytorch