TY - GEN
T1 - A Real-time and Unsupervised Advancement Scheme for Underwater Machine Vision
AU - Chen, Xingyu
AU - Wu, Zhengxing
AU - Yu, Junzhi
AU - Wen, Li
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/8/24
Y1 - 2018/8/24
N2 - This paper presents a real-time and unsupervised advancement scheme (RUAS) for underwater machine vision in the natural light condition. RUAS consists of three steps, pre-searching, restoration, and post-enhancing. In pre-searching, we provide a Protected and Greedy Artificial Fish School Algorithm (PGAFSA) to optimize the key parameters of the underwater images, and design an evaluating indicator for the PGAFSA based on the features of underwater images. During the restoration, an image degeneration model is built and the Wiener Filter is employed for noise suppression. Moreover, a filtering-aided color correlation method (FCCM) is then presented against color absorption caused by water. The contrast limited adaptive histogram equalization is employed for the contrast stretch in post-enhancing. Finally, we validated the effectiveness and feasibility of the proposed RUAS with deep-sea environmental videos and practical underwater environments.
AB - This paper presents a real-time and unsupervised advancement scheme (RUAS) for underwater machine vision in the natural light condition. RUAS consists of three steps, pre-searching, restoration, and post-enhancing. In pre-searching, we provide a Protected and Greedy Artificial Fish School Algorithm (PGAFSA) to optimize the key parameters of the underwater images, and design an evaluating indicator for the PGAFSA based on the features of underwater images. During the restoration, an image degeneration model is built and the Wiener Filter is employed for noise suppression. Moreover, a filtering-aided color correlation method (FCCM) is then presented against color absorption caused by water. The contrast limited adaptive histogram equalization is employed for the contrast stretch in post-enhancing. Finally, we validated the effectiveness and feasibility of the proposed RUAS with deep-sea environmental videos and practical underwater environments.
UR - https://www.scopus.com/pages/publications/85050873714
U2 - 10.1109/CYBER.2017.8446363
DO - 10.1109/CYBER.2017.8446363
M3 - 会议稿件
AN - SCOPUS:85050873714
SN - 9781538604892
T3 - 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
SP - 271
EP - 276
BT - 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
Y2 - 31 July 2017 through 4 August 2017
ER -