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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

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Image Deblurring using Generative Adversarial Networks

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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:

 



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)