DeblurGAN

arXiv Paper Version

Pytorch implementation of the paper DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks.

Our networks takes blurry image as an input and procude the cirresponding sharp estimate, as in example:

Blurred Photo

Restored using out method

Sharp photo

Our model is Conditional Wasserstein GAN with Gradient Penalty + Perceptual loss based on VGG-19 activations

How to run

Prerequisites

• NVIDIA GPU + CUDA CuDNN (CPU untested, feedback appreciated)
• Pytorch

Download weights from Dropbox 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

Note: The repository is still being structured, the links to the data, weights and also instructions would be updated soon

Acknowledgments

Code borrows heavily from pix2pix.