Knowledge Commons of Institute of Automation，CAS
| Towards Real-Time Advancement of Underwater Visual Quality With GAN | |
Chen, Xingyu1,2 ; Yu, Junzhi1,2,3; Kong, Shihan1,2; Wu, Zhengxing1,2; Fang, Xi4; Wen, Li4
| |
| Source Publication | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
 |
| ISSN | 0278-0046 |
| 2019-12-01 | |
| Volume | 66Issue:12Pages:9350-9359 |
| Corresponding Author | Yu, Junzhi(junzhi.yu@ia.ac.cn) |
| Abstract | 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). |
| Keyword | Generative adversarial networks (GAN) image restoration machine learning underwater vision |
| DOI | 10.1109/TIE.2019.2893840 |
| WOS Keyword | IMAGE-ENHANCEMENT |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | National Natural Science Foundation of China ; Beijing Natural Science Foundation |
| WOS Research Area | Automation & Control Systems ; Engineering ; Instruments & Instrumentation |
| WOS Subject | Automation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation |
| WOS ID | WOS:000480309400023 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/27563 |
| Collection | 中国科学院自动化研究所 |
| Corresponding Author | Yu, Junzhi |
| Affiliation | 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 |
| First Author Affilication | Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| Corresponding Author Affilication | Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| Recommended Citation 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. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment