# DeblurGAN

**Repository Path**: xiaolanyu666/DeblurGAN

## Basic Information

- **Project Name**: DeblurGAN
- **Description**: Image Deblurring using Generative Adversarial Networks
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-11-27
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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:
<img src="images/animation3.gif" width="400px"/> <img src="images/animation4.gif" width="400px"/>


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)