ryo-abiko / GCNet

Single Image Reflection Removal based on GAN with Gradient Constraint (GCNet)

GCNet

Single Image Reflection Removal based on GAN with Gradient Constraint

Input real image	Image generated by our method

The sample image is provided by SIR² benchmark dataset.

Requirements

Python

• Pytorch (torch & torchvision)
• numpy
• skimage
• tqdm

Usage

Put input images into images/<your_dataset_name>/input/. Processed images are saved in images/<your_dataset_name>/output/.

If you have ground truth images, put them into images/<your_dataset_name>/gt/. PSNR and SSIM will be calculated. The file name of ground truth images should match with those of input images.

Run python3 demo.py --dataset_name=<your_dataset_name>.

Citation

Please cite this paper if you use this code.

@ARTICLE{abiko2019reflection,
author={R. {Abiko} and M. {Ikehara}},
journal={IEEE Access},
title={Single Image Reflection Removal Based on GAN With Gradient Constraint},
year={2019},
volume={7},
number={},
pages={148790-148799},
keywords={Generative adversarial networks;Training;Generators;Feature extraction;Correlation;Glass;Task analysis;Image restoration;deep learning;reflection removal;image separation;generative adversarial network},
doi={10.1109/ACCESS.2019.2947266},
ISSN={},
month={},}

For further information, please contact: {abiko, ikehara}@tkhm.elec.keio.ac.jp