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Revision Accepted: 2020-07-12

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Jing-chun Zhou


Wei-shi Zhang


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.12 P.1745-1769


Classical and state-of-the-art approaches for underwater image defogging: a comprehensive survey

Author(s):  Jing-chun Zhou, De-huan Zhang, Wei-shi Zhang

Affiliation(s):  College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China

Corresponding email(s):   zhoujingchun@dlmu.edu.cn, zhangdehuan@dlmu.edu.cn, teesiv@dlmu.edu.cn

Key Words:  Underwater image defogging, Restoration approaches, Enhancement approaches, Evaluation metrics

Jing-chun Zhou, De-huan Zhang, Wei-shi Zhang. Classical and state-of-the-art approaches for underwater image defogging: a comprehensive survey[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(12): 1745-1769.

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%T Classical and state-of-the-art approaches for underwater image defogging: a comprehensive survey
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%A Wei-shi Zhang
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T1 - Classical and state-of-the-art approaches for underwater image defogging: a comprehensive survey
A1 - Jing-chun Zhou
A1 - De-huan Zhang
A1 - Wei-shi Zhang
J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2000190

In underwater scenes, the quality of the video and image acquired by the underwater imaging system suffers from severe degradation, influencing target detection and recognition. Thus, restoring real scenes from blurred videos and images is of great significance. Owing to the light absorption and scattering by suspended particles, the images acquired often have poor visibility, including color shift, low contrast, noise, and blurring issues. This paper aims to classify and compare some of the significant technologies in underwater image defogging, presenting a comprehensive picture of the current research landscape for researchers. First we analyze the reasons for degradation of underwater images and the underwater optical imaging model. Then we classify the underwater image defogging technologies into three categories, including image restoration approaches, image enhancement approaches, and deep learning approaches. Afterward, we present the objective evaluation metrics and analyze the state-of-the-art approaches. Finally, we summarize the shortcomings of the defogging approaches for underwater images and propose seven research directions.





Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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