Awesome

Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data

by Xiaowei Hu, Yitong Jiang, Chi-Wing Fu, and Pheng-Ann Heng

This implementation is written by Xiaowei Hu at the Chinese University of Hong Kong.

USR Dataset

Our USR dataset is available for download at Google Drive.

Results

Please find the new results at https://github.com/xw-hu/Unveiling-Deep-Shadows.

Prerequisites

• Python 3.5
• PyTorch 1.0
• torchvision
• numpy

Train

1. Select the training sets (USR, SRD, or ISTD ) and set the path of the dataset in train_Mask-ShadowGAN.py
2. Run train_Mask-ShadowGAN.py

Test

1. Select the testing sets (USR, SRD, or ISTD ) and set the path of the dataset in test.py
2. Run test.py

Bibtex

If you find our work, code, dataset, or results useful, please consider citing our paper as follows:

@inproceedings{hu2019mask,        
  title={{Mask-ShadowGAN}: Learning to Remove Shadows from Unpaired Data},         
  author={Hu, Xiaowei and Jiang, Yitong and Fu, Chi-Wing and Heng, Pheng-Ann},         
  booktitle={ICCV},       
  year={2019}
}

Acknowledgments

Code is implemented based on a clean and readable Pytorch implementation of CycleGAN. We would like to thank Aitor Ruano and the authors of CycleGAN, Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros.