DD-cGAN

Dual-Domain Single Image De-Raining using Conditional Generative Adversarial Networks

Prasen Kumar Sharma, Priyankar Jain, Arijit Sur

[Paper Link] (ICIP'19)

Complete training code coming soon...

@inproceedings{8803353,
  author={P. K. {Sharma} and P. {Jain} and A. {Sur}}, 
      booktitle={2019 IEEE International Conference on Image Processing (ICIP)}, 
      title={Dual-Domain Single Image De-Raining Using Conditional Generative Adversarial Network}, 
   year={2019}, 
   volume={}, 
   number={}, 
   pages={2796-2800}, 
   keywords={Image De-raining;Conditional Generative Adversarial Network (cGAN);Haar Wavelets;Perceptual Loss}, 
   doi={10.1109/ICIP.2019.8803353}, 
   ISSN={2381-8549}, 
   month={Sep.},}

Prerequisites:

1. Linux
2. Python 2 or 3
3. CPU or NVIDIA GPU + CUDA CuDNN (CUDA 8.0)

Installation:

1. Install Tensorflow and dependencies from https://www.tensorflow.org/install (conda install -c conda-forge tensorflow)

2. Install python packages: numpy, scipy, PIL, pdb, sewar

Demo using pre-trained model

python3 testing.py

Pre-trained model can be downloaded at (put it in the folder 'models'): https://drive.google.com/drive/folders/13WJn0gjpanrhd07Rv3oO3sHb0i3KaTtv?usp=sharing

Pre-trained models related to wavelets can be downloaded at (put it in the folder 'sub-bands-npzs'): https://drive.google.com/drive/folders/1LwqnsJqvCKA-BP44otLJuA1X1Udg2WGj?usp=sharing

Dataset

Training (heavy, medium, light) and testing (TestA and Test B) data can be downloaded at the following link: https://drive.google.com/file/d/1cMXWICiblTsRl1zjN8FizF5hXOpVOJz4/view?usp=sharing

Acknowledgments

Great thanks to He Zhang for dataset. Codes heavily borrowed from DDN