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Deep images enhancement for turbid underwater images based on unsupervised learning

文献类型：期刊论文

作者Zhou, Wen-Hui3; Zhu, Deng-Ming3; Shi, Min2; Li, Zhao-Xin3; Duan, Ming1; Wang, Zhao-Qi3; Zhao, Guo-Liang2; Zheng, Cheng-Dong2
刊名COMPUTERS AND ELECTRONICS IN AGRICULTURE
出版日期2022-11-01
卷号202页码:10
关键词Image enhancement Visual perception Underwater dataset Deep learning
ISSN号0168-1699
DOI10.1016/j.compag.2022.107372
英文摘要In agriculture, aquaculture technologies such as precise feeding, fish identification and fishing based on underwater machine vision all rely on the analysis of underwater images. However, due to the scatting and attenuation of the illumination in the real-world underwater environment, turbid underwater images are inevitably degraded, limiting their applicability in many vision tasks. In this paper, we present an unsupervised deep learning framework, called Underwater Loop Enhancement Network (ULENet), to improve the quality of turbid underwater images. We first propose an underwater dataset construction scheme and construct the dataset on which the network proposed above is trained. The underwater dataset contains images of three different scenes: lake and reservoir scene data (no label), pool scene data (weakly correlated label), and laboratory scene data (strongly correlated label). Then we propose a loop enhancement structure that uses the approximate candidates as labels and improves the visual quality of the image through the iterative training process. We formulate a new underwater visual perception loss function that evaluates the perceptual image quality based on its color, contrast, saturation and clarity. During the training process, a more realistic, higher -contrast, and clearer underwater image is gradually generated. Qualitative and quantitative evaluations show that the proposed method can effectively enhance image clarity. Moreover, the enhanced images are applied to several vision tasks to achieve better results, such as edge detection, key point matching, fish target detection and saliency prediction etc.
资助项目National Key R&D Program of China ; Scientific Research Instrument and Equipment Development Project of Chinese Academy of Sciences ; [2020YFB1710400] ; [YJKYYQ20190055]
WOS研究方向Agriculture ; Computer Science
语种英语
WOS记录号WOS:000868790000004
出版者ELSEVIER SCI LTD
源URL[http://119.78.100.204/handle/2XEOYT63/19766]  
专题中国科学院计算技术研究所期刊论文
通讯作者Zhu, Deng-Ming
作者单位1.Chinese Acad Sci, Inst Hydrobiol, Wuhan 430072, Peoples R China
2.North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhou, Wen-Hui,Zhu, Deng-Ming,Shi, Min,et al. Deep images enhancement for turbid underwater images based on unsupervised learning[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2022,202:10.
APA Zhou, Wen-Hui.,Zhu, Deng-Ming.,Shi, Min.,Li, Zhao-Xin.,Duan, Ming.,...&Zheng, Cheng-Dong.(2022).Deep images enhancement for turbid underwater images based on unsupervised learning.COMPUTERS AND ELECTRONICS IN AGRICULTURE,202,10.
MLA Zhou, Wen-Hui,et al."Deep images enhancement for turbid underwater images based on unsupervised learning".COMPUTERS AND ELECTRONICS IN AGRICULTURE 202(2022):10.

入库方式： OAI收割

来源：计算技术研究所

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