CASIA OpenIR
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 PublicationIEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
ISSN0278-0046
2019-12-01
Volume66Issue:12Pages:9350-9359
Corresponding AuthorYu, Junzhi(junzhi.yu@ia.ac.cn)
AbstractLow 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).
KeywordGenerative adversarial networks (GAN) image restoration machine learning underwater vision
DOI10.1109/TIE.2019.2893840
WOS KeywordIMAGE-ENHANCEMENT
Indexed BySCI
Language英语
Funding ProjectNational 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 OrganizationNational Natural Science Foundation of China ; Beijing Natural Science Foundation
WOS Research AreaAutomation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS SubjectAutomation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS IDWOS:000480309400023
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27563
Collection中国科学院自动化研究所
Corresponding AuthorYu, Junzhi
Affiliation1.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 AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese 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.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Chen, Xingyu]'s Articles
[Yu, Junzhi]'s Articles
[Kong, Shihan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Chen, Xingyu]'s Articles
[Yu, Junzhi]'s Articles
[Kong, Shihan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Chen, Xingyu]'s Articles
[Yu, Junzhi]'s Articles
[Kong, Shihan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.