{"type":"ScholarlyArticle","title":"A Comprehensive Survey on Image Dehazing Based on Deep Learning","authors":[{"name":"Jie Gui","slug":"jie-gui","given":"Jie","family":"Gui","affiliation":""},{"name":"Xiaofeng Cong","slug":"xiaofeng-cong","given":"Xiaofeng","family":"Cong","affiliation":""},{"name":"Yuan Cao","slug":"yuan-cao","given":"Yuan","family":"Cao","affiliation":""},{"name":"Wenqi Ren","slug":"wenqi-ren","given":"Wenqi","family":"Ren","affiliation":""},{"name":"Jun Zhang","slug":"jun-zhang","given":"Jun","family":"Zhang","affiliation":""},{"name":"Jing Zhang","slug":"jing-zhang","given":"Jing","family":"Zhang","affiliation":""},{"name":"Dacheng Tao","slug":"dacheng-tao","given":"Dacheng","family":"Tao","affiliation":""}],"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.","doi":"10.24963/ijcai.2021/604","url":"https://ijcai.org/proceedings/2021/604","pdf":"https://ijcai.org/proceedings/2021/0604.pdf","markdown":"https://ijcai.org/proceedings/2021/604.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/604","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Survey Track","track":"Survey Track","number":604,"pages":{"first":4426,"last":4433},"keywords":["Machine learning: General","Computer vision: General"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}