A Comprehensive Survey on Image Dehazing Based on Deep Learning

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.

Authors

• Jie Gui School of Cyber Science and Engineering, Southeast University; Engineering Research Center of Blockchain Application, Supervision And Management (Southeast University), Ministry of Education; Purple Mountain Laboratories
• Xiaofeng Cong School of Cyber Science and Engineering, Southeast University, China
• Yuan Cao School of Computer Science and Technology, Ocean University of China
• Wenqi Ren SUN YAT-SEN UNIVERSITY
• Jun Zhang The Hong Kong University of Science and Technology
• Jing Zhang
• Dacheng Tao College of Computing and Data Science, Nanyang Technological University

Keywords

• Computer vision: General
• Machine learning: General