Image Deblurring using Generative Adversarial Networks
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README.md

DeblurGAN

arXiv Paper Version

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 Dropbox . Note that during the inference you need to keep only Generator weights.

Put the weights into

/.checkpoints/experiment_name

To test a model put your blurry images into a folder and run:

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

Train

If you want to train the model on your data run the following command to create image pairs:

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

python train.py --dataroot /.path_to_your_data --learn_residual --resize_or_crop crop --fineSize CROP_SIZE (we used 256)

Other Implementations

Keras Blog

Keras Repository

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. The images were taken from GoPRO test dataset - DeepDeblur