You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network (YOLY)

Pytorch implementation for YOLY (IJCV 2021) [paper]

Dependencies

• Python == 3.6.10
• Pytorch == 1.1.0
• opencv-python == 3.4.2.16
• opencv-contrib-python == 3.4.2.16

We also export our conda virtual environment as YOLY.yaml. You can use the following command to create the environment.

conda env create -f YOLY.yaml

Demo

You can use the following command to dehaze test images in ./data:

python dehazing.py

If you want to test YOLY on a real world image which does not have ground truth. You can use the following command:

python RW_dehazing.py

The only difference between two command is whether the program calculates PSNR and SSIM.

Citation

If you find YOLY useful in your research, please consider citing:

@article{Li:2021kt,
author = {Li, Boyun and Gou, Yuanbiao and Gu, Shuhang and Liu, Jerry Zitao and Zhou, Joey Tianyi and Peng, Xi},
title = {{You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network}},
journal = {International Journal of Computer Vision},
year = {2021},
pages = {1--14},
month = mar
}