Towards Real-Time Advancement of Underwater Visual Quality With GAN
文献类型：期刊论文
| 作者 | Chen, Xingyu1,2 ; Yu, Junzhi1,2,3; Kong, Shihan1,2; Wu, Zhengxing1,2; Fang, Xi4; Wen, Li4
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| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
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| 出版日期 | 2019-12-01 |
| 卷号 | 66期号:12页码:9350-9359 |
| 关键词 | Generative adversarial networks (GAN) image restoration machine learning underwater vision |
| ISSN号 | 0278-0046 |
| DOI | 10.1109/TIE.2019.2893840 |
| 通讯作者 | Yu, Junzhi(junzhi.yu@ia.ac.cn) |
| 英文摘要 | Low visual quality has prevented underwater robotic vision from a wide range of applications. Although several algorithms have been developed, real time and adaptive methods are deficient for real-world tasks. In this paper, we address this difficulty based on generative adversarial networks (GAN), and propose a GAN-based restoration scheme (GAN-RS). In particular, we develop a multibranch discriminator including an adversarial branch and a critic branch for the purpose of simultaneously preserving image content and removing underwater noise. In addition to adversarial learning, a novel dark channel prior loss also promotes the generator to produce realistic vision. More specifically, an underwater index is investigated to describe underwater properties, and a loss function based on the underwater index is designed to train the critic branch for underwater noise suppression. Through extensive comparisons on visual quality and feature restoration, we confirm the superiority of the proposed approach. Consequently, the GAN-RS can adaptively improve underwater visual quality in real time and induce an overall superior restoration performance. Finally, a real-world experiment is conducted on the seabed for grasping marine products, and the results are quite promising. The source code is publicly available(1). |
| WOS关键词 | IMAGE-ENHANCEMENT |
| 资助项目 | National Natural Science Foundation of China[61633004] ; National Natural Science Foundation of China[61725305] ; National Natural Science Foundation of China[61603388] ; National Natural Science Foundation of China[61633017] ; Beijing Natural Science Foundation[4161002] |
| WOS研究方向 | Automation & Control Systems ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000480309400023 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Beijing Natural Science Foundation |
| 源URL | [http://ir.ia.ac.cn/handle/173211/27562]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室 |
| 通讯作者 | Yu, Junzhi |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Peking Univ, Beijing Innovat Ctr Engn Sci & Adv Technol, Beijing 100871, Peoples R China 4.Beihang Univ, Sch Mech Engn & Automat, Beijing 100191, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Xingyu,Yu, Junzhi,Kong, Shihan,et al. Towards Real-Time Advancement of Underwater Visual Quality With GAN[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2019,66(12):9350-9359. |
| APA | Chen, Xingyu,Yu, Junzhi,Kong, Shihan,Wu, Zhengxing,Fang, Xi,&Wen, Li.(2019).Towards Real-Time Advancement of Underwater Visual Quality With GAN.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,66(12),9350-9359. |
| MLA | Chen, Xingyu,et al."Towards Real-Time Advancement of Underwater Visual Quality With GAN".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 66.12(2019):9350-9359. |
入库方式： OAI收割
来源：自动化研究所
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