A Comprehensive Survey on Image Dehazing Based on Deep Learning
Jie Gui, Xiaofeng Cong, Yuan Cao, Wenqi Ren, Jun Zhang, Jing Zhang, Dacheng Tao
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.
BibTeX
@inproceedings{ijcai2021p604,
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 = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Zhi-Hua Zhou},
pages = {4426--4433},
year = {2021},
month = {8},
note = {Survey Track},
doi = {10.24963/ijcai.2021/604},
url = {https://doi.org/10.24963/ijcai.2021/604},
}