Image De-raining Using a Conditional Generative Adversarial Network

Image De-raining Using a Conditional Generative Adversarial Network

[Paper Link]

[Project Page]

He Zhang, Vishwanath Sindagi, Vishal M. Patel

In this paper, we investigate a new point of view in addressing single image de-raining problem. Instead of focusing only on deciding what is a good prior or a good framework to achieve good quantitative and qualitative performance, we also ensure that the de-rained image does not degrade the performance of a given computer vision algorithm such as detection and classification. In other words, the de-rained result should be indistinguishable from its corresponding clear image to a given discriminator. This criterion can be directly incorporated into the optimization framework by using the recently introduced conditional generative adversarial networks (GANs). To minimize artifacts introduced by GANs and ensure better visual quality, a new refined loss function is introduced.

@article{zhang2017image,		
  title={Image De-raining Using a Conditional Generative Adversarial Network},
  author={Zhang, He and Sindagi, Vishwanath and Patel, Vishal M},
  journal={arXiv preprint arXiv:1701.05957},
  year={2017}
} 

Prepare

Instal torch7

Install nngraph

Install hdf5

Download the dataset from (https://drive.google.com/drive/folders/0Bw2e6Q0nQQvGbi1xV1Yxd09rY2s?resourcekey=0-dUoT9AJl1q6fXow9t5TcRQ&usp=sharing) and put the dataset folder into the "IDCGAN" folder

Training

DATA_ROOT=./datasets/rain name=rain which_direction=BtoA th train.lua

Testing

DATA_ROOT=./datasets/rain name=rain which_direction=BtoA phase=test_nature th test.lua

Testing using ID-CGAN model

The trained ID-CGAN model and our training and testing datasets can be found at (https://drive.google.com/drive/folders/0Bw2e6Q0nQQvGbi1xV1Yxd09rY2s?resourcekey=0-dUoT9AJl1q6fXow9t5TcRQ&usp=sharing)

*Make sure you download the vgg model that used for perceotual loss and put it in the ./IDCGAN/per_loss/models

##Acknowledgments##

Code borrows heavily from [pix2pix] and [Perceptual Loss]. Thanks for the sharing.