Towards Real-Time Advancement of Underwater Visual Quality With GAN | |
Chen, Xingyu1,2![]() ![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
![]() |
ISSN | 0278-0046 |
2019-12-01 | |
卷号 | 66期号:12页码:9350-9359 |
通讯作者 | Yu, Junzhi(junzhi.yu@ia.ac.cn) |
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). |
关键词 | Generative adversarial networks (GAN) image restoration machine learning underwater vision |
DOI | 10.1109/TIE.2019.2893840 |
关键词[WOS] | IMAGE-ENHANCEMENT |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61633004] ; National Natural Science Foundation of China[61725305] ; National Natural Science Foundation of China[61633017] ; National Natural Science Foundation of China[61603388] ; Beijing Natural Science Foundation[4161002] ; Beijing Natural Science Foundation[4161002] ; National Natural Science Foundation of China[61603388] ; National Natural Science Foundation of China[61633017] ; National Natural Science Foundation of China[61725305] ; National Natural Science Foundation of China[61633004] |
项目资助者 | National Natural Science Foundation of China ; Beijing Natural Science Foundation |
WOS研究方向 | Automation & Control Systems ; Engineering ; Instruments & Instrumentation |
WOS类目 | Automation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation |
WOS记录号 | WOS:000480309400023 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/27563 |
专题 | 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 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
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. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
GAN-RS_TIE.pdf(4984KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论