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

The code used to create the paper contains a bug in PSNR calculation, found by Huaijin Chen from Rice university, results recalculation are present below.

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)

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