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Unsupervised Domain-Specific Deblurring via Disentangled Representations

Pytorch implementation of the paper Unsupervised Domain-Specific Deblurring via Disentangled Representations. The proposed method is unsupervised and takes blurred domain-specific image (faces or text) as an input and procude the corresponding sharp estimate.

contact: bylu@umiacs.umd.edu

Sample Results

TO BE ADDED...

Dataset

To train the model, unpaired sharp and blurred images folders should be named in the following format: datasets/name/trainA and datasets/name/trainB. Test images can be stored in the same folder and you may choose your own folder name.

Usage

Data Preperation

In our experiment, face data is from CelebA dataset and text data is from BMVC text dataset. To manually blur the images, we use the method proposed in DeblurGAN. The dataset are randomly split into three subsets: trainA (sharp), trainB(blur) and test set.

Train

To train the model, run the following command line in the source code directory. You may set other parameters based on your experiment setting.

python train.py --dataroot ../datasets/DatasetName/ --name job_name --batch_size 2 --lambdaB 0.1 --lr 0.0002

Test

Our pre-trained model for face and text can be downloaded here. To test the model, run the following command line in the source code directory. You may set other parameters based on your experiment setting. To choose the perceptual loss type as face, you need to manually set the VGG face model path in network.py. VGG_face pretrained model can be found here.

python test.py --dataroot ../datasets/dataset_name/test_blur/ --num 1 --resume ../results/model/locations --name job_name --orig_dir ../datasets/dataset_name/test_orig --percep face

Citation

If you find the code helpful in your research or work, please kindly cite our paper.

@inproceedings{lu2019unsupervised,
  title={Unsupervised domain-specific deblurring via disentangled representations},
  author={Lu, Boyu and Chen, Jun-Cheng and Chellappa, Rama},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={10225--10234},
  year={2019}
}

Acknowledgments

The code borrows heavily from DRIT. We use the image blurring method in DeblurGAN.