A Real-time and Unsupervised Advancement Scheme for Underwater Machine Vision

被引：0
|
作者：
Che, Xingyu [1 ,2 ]
Wu, Zhengxing [2 ]
Yu, Junzhi [2 ]
Wen, Li [3 ]
机构：
[1] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[2] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
[3] Beihang Univ, Sch Mech Engn & Automat, Beijing 100191, Peoples R China
来源：
2017 IEEE 7TH ANNUAL INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (CYBER) | 2017年
基金：
北京市自然科学基金; 中国国家自然科学基金;
关键词：
IMAGE-ENHANCEMENT;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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, presearching, 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 liltering-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.
引用
收藏
页码：271 / 276
页数：6
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