A Pytorch implementation for DehazeNet in paper 'DehazeNet: An End-to-End System for Single Image Haze Removal'

@article{cai2016dehazenet,
author = {Bolun Cai, Xiangmin Xu, Kui Jia, Chunmei Qing and Dacheng Tao},
title={DehazeNet: An End-to-End System for Single Image Haze Removal},
journal={IEEE Transactions on Image Processing},
year={2016},
volume={25},
number={11},
pages={5187-5198},
}

Run create_dataset.py to create a training dataset.
Run DehazeNet-pytorch.py.train() to train a model.
Run DehazeNet-pytorch.py.defog() to defog a picture.
The model is trained on GPU and defog() is run on CPU.

The model I trained didn't work well.
In the paper, 'To refine the transmission map, guided image filtering [15] is used to smooth the image.', but I did't do it.