@inproceedings{b11bdff258894debaf216ed583236eb0,
title = "Unsupervised Conditional Disentangle Network for Image Dehazing",
abstract = "Image dehazing aims to restore the blurry image information caused by the ambiguities of unknown scene radiance and transmission. Instead of using paired images or depth information, we propose an Unsupervised Conditional Disentangle Network (UCDN) using unpaired dataset. Our approach enforces the constraint by introducing physical-based disentanglement. Unlike other unsupervised dehazing models, our approach adapts the multi-concentration of fog and outperforms on the dataset with different concentrations. Extensive experiments on synthesized dataset demonstrate that our approach can surpass state-of-the-arts. Meanwhile, through benchmarking on our collected natural hazy dataset, our approach can generate more perceptually appealing dehazing results.",
keywords = "GAN, image enhancing, natural hazy dataset, single image dehazing, unsupervised",
author = "Yizhou Jin and Guangshuai Gao and Qingjie Liu and Yunhong Wang",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Image Processing, ICIP 2020 ; Conference date: 25-09-2020 Through 28-09-2020",
year = "2020",
month = oct,
doi = "10.1109/ICIP40778.2020.9190833",
language = "英语",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "963--967",
booktitle = "2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings",
address = "美国",
}