https://github.com/KupynOrest/DeblurGAN

Image Deblurring using Generative Adversarial Networks
https://github.com/KupynOrest/DeblurGAN

blurry-images computer-vision convolutional-networks convolutional-neural-networks deblurring deep-learning gan image-manipulation image-processing image-to-image-translation neural-network paper pix2pix pytorch

Last synced: 5 days ago
JSON representation

Image Deblurring using Generative Adversarial Networks

• Host: GitHub
• URL: https://github.com/KupynOrest/DeblurGAN
• Owner: KupynOrest
• License: other
• Created: 2017-04-26T14:08:11.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2019-12-25T18:40:00.000Z (almost 5 years ago)
• Last Synced: 2024-10-29T17:49:10.923Z (6 days ago)
• Topics: blurry-images, computer-vision, convolutional-networks, convolutional-neural-networks, deblurring, deep-learning, gan, image-manipulation, image-processing, image-to-image-translation, neural-network, paper, pix2pix, pytorch
• Language: Python
• Homepage:
• Size: 83.8 MB
• Stars: 2,508
• Watchers: 61
• Forks: 517
• Open Issues: 146
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-cv - Image Deblurring using Generative Adversarial Networks
• awesome-cv - Image Deblurring using Generative Adversarial Networks

README

        
# 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:

 

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)