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CLC number: TP391.4

On-line Access: 2020-12-10

Received: 2020-04-23

Revision Accepted: 2020-07-12

Crosschecked: 2020-11-11

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jing-chun Zhou

https://orcid.org/0000-0002-4111-6240

Wei-shi Zhang

https://orcid.org/0000-0003-0519-8397

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

http://doi.org/10.1631/FITEE.2000190


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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Abstract: 
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.

经典和先进的水下图像去雾方法综述

周景春,张得欢,张维石
大连海事大学信息科学技术学院,中国大连市,116026

摘要:在水下场景中,成像系统获取的视频和图像质量严重下降,影响目标的检测和识别。因此,从模糊的视频和图像中恢复真实场景具有重要意义。由于悬浮粒子对光线的吸收和散射作用,获取的图像往往能见度低,存在偏色、对比度低、噪声和模糊等问题。本文旨在对水下图像去雾的重要技术进行分类和比较,为本领域研究人员分析当前研究现状。通过广泛的文献调研,首先分析水下图像退化原因和水下光学成像模型。将现有水下图像去雾方法分为3类,包括水下图像复原方法、水下图像增强方法和基于深度学习的去雾方法。然后,总结水下图像客观质量评价方法,并对经典和当前流行的水下图像去雾方法的性能进行比较分析。最后指出当前水下图像复原和增强技术存在的不足,展望未来的7个研究方向。

关键词:水下图像去雾;复原方法;增强方法;评价指标

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

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