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Beyond Single Reference for Training: Underwater Image Enhancement via Comparative Learning

文献类型：期刊论文

作者Li, Kunqian1; Wu, Li1; Qi, Qi4; Liu, Wenjie1; Gao, Xiang3; Zhou, Liqin1; Song, Dalei1,2
刊名IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
出版日期2023-06-01
卷号33期号:6页码:2561-2576
ISSN号1051-8215
关键词Training Image enhancement Visualization Task analysis Generators Deep learning Oceans Underwater image enhancement deep learning convolutional neural network comparative learning blind image quality assessment
DOI10.1109/TCSVT.2022.3225376
通讯作者Song, Dalei(songdalei@ouc.edu.cn)
英文摘要Due to the wavelength-dependent light absorption and scattering, the raw underwater images are usually inevitably degraded. Underwater image enhancement (UIE) is of great importance for underwater observation and operation. Data-driven methods, such as deep learning-based UIE approaches, tend to be more applicable to real underwater scenarios. However, the training of deep models is limited by the extreme scarcity of underwater images with enhancement references, resulting in their poor performance in dynamic and diverse underwater scenes. As an alternative, enhancement reference achieved by volunteer voting alleviate the sample shortage to some extent. Since such artificially acquired references are not veritable ground truth, they are far from complete and accurate to provide correct and rich supervision for the enhancement model training. Beyond training with single reference, we propose the first comparative learning framework for UIE problem, namely CLUIE-Net, to learn from multiple candidates of enhancement reference. This new strategy also supports semi-supervised learning mode. Besides, we propose a regional quality-superiority discriminative network (RQSD-Net) as an embedded quality discriminator for the CLUIE-Net. Comprehensive experiments demonstrate the effectiveness of RQSD-Net and the comparative learning strategy for UIE problem. The code, models and new dataset RQSD-UI are available at: https://justwj.github.io/CLUIE-Net.html/.
WOS关键词QUALITY ASSESSMENT ; NEURAL-NETWORK ; CHALLENGES ; WATER
资助项目National Natural Science Foundation of China[61906177] ; Natural Science Foundation of Shandong Province[ZR2019BF034] ; Fundamental Research Funds for the Central Universities[201964013]
WOS研究方向Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001004257300003
资助机构National Natural Science Foundation of China ; Natural Science Foundation of Shandong Province ; Fundamental Research Funds for the Central Universities
源URL[http://ir.ia.ac.cn/handle/173211/53747]  
专题中科院工业视觉智能装备工程实验室
通讯作者Song, Dalei
作者单位1.Ocean Univ China, Coll Engn, Qingdao 266100, Peoples R China
2.Ocean Univ China, Inst Adv Ocean Study, Qingdao 266100, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
4.Ocean Univ China, Coll Comp Sci & Technol, Qingdao 266100, Peoples R China
推荐引用方式
GB/T 7714		 Li, Kunqian,Wu, Li,Qi, Qi,et al. Beyond Single Reference for Training: Underwater Image Enhancement via Comparative Learning[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2023,33(6):2561-2576.
APA Li, Kunqian.,Wu, Li.,Qi, Qi.,Liu, Wenjie.,Gao, Xiang.,...&Song, Dalei.(2023).Beyond Single Reference for Training: Underwater Image Enhancement via Comparative Learning.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,33(6),2561-2576.
MLA Li, Kunqian,et al."Beyond Single Reference for Training: Underwater Image Enhancement via Comparative Learning".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 33.6(2023):2561-2576.

入库方式： OAI收割

来源：自动化研究所

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