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- Jie Gui
- Southeast University
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- Xiaofeng Cong
- AnHui University
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- Yuan Cao
- Ocean University of China
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- Wenqi Ren
- Institute of Information Engineering, Chinese Academy of Sciences
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- Jun Zhang
- AnHui University
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- Jing Zhang
- University of Sydney
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- Dacheng Tao
- JD Explore Academy, JD.com, China
説明
<jats:p>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.</jats:p>
収録刊行物
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- Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence 2021-08
International Joint Conferences on Artificial Intelligence Organization
