A Comprehensive Survey on Image Dehazing Based on Deep Learning
Abstract
The presence of haze significantly reduces the quality of images. Researchers have designed a variety of algorithms for image dehazing (ID) to restore the quality of hazy images. However, there are few studies that summarize the deep learning (DL) based dehazing technologies. In this paper, we conduct a comprehensive survey on the recent proposed dehazing methods. Firstly, we conclude the commonly used datasets, loss functions and evaluation metrics. Secondly, we group the existing researches of ID into two major categories: supervised ID and unsupervised ID. The core ideas of various influential dehazing models are introduced. Finally, the open issues for future research on ID are pointed out.
Cite
Text
Gui et al. "A Comprehensive Survey on Image Dehazing Based on Deep Learning." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/604Markdown
[Gui et al. "A Comprehensive Survey on Image Dehazing Based on Deep Learning." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/gui2021ijcai-comprehensive/) doi:10.24963/IJCAI.2021/604BibTeX
@inproceedings{gui2021ijcai-comprehensive,
title = {{A Comprehensive Survey on Image Dehazing Based on Deep Learning}},
author = {Gui, Jie and Cong, Xiaofeng and Cao, Yuan and Ren, Wenqi and Zhang, Jun and Zhang, Jing and Tao, Dacheng},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2021},
pages = {4426-4433},
doi = {10.24963/IJCAI.2021/604},
url = {https://mlanthology.org/ijcai/2021/gui2021ijcai-comprehensive/}
}