Underwater Image Enhancement via Physical-Feedback Adversarial Transfer Learning
文献类型：期刊论文
| 作者 | Zhou, Yuan1; Yan, Kangming2; Li, Xiaofeng3 |
| 刊名 | IEEE JOURNAL OF OCEANIC ENGINEERING
 |
| 出版日期 | 2021-09-23 |
| 页码 | 12 |
| 关键词 | Degradation Adaptation models Image restoration Image color analysis Image enhancement Convolutional neural networks Data models Degradation model domain adaptation generative adversarial networks underwater image enhancement |
| ISSN号 | 0364-9059 |
| DOI | 10.1109/JOE.2021.3104055 |
| 通讯作者 | Li, Xiaofeng(xiaofeng.li@ieee.org) |
| 英文摘要 | This article proposes a domain adaptive learning framework based on physical model feedback for underwater image enhancement. Underwater image enhancement involves mapping from low-quality underwater images to their dewatered counterparts. Due to the lack of dewatered images as ground truth, most learning-based methods are trained using synthetic datasets. However, they usually ignored the domain gap between synthetic training data and real-world testing data, which seriously reduces the generalization ability of those models when testing on real underwater images. We solve the problem by embedding a domain adaptive mechanism in a learning framework to eliminate the domain gap. However, the basic formulation of a domain adaptive-based learning framework does not generate realistic images in color and details. Motivated by an observation that the estimated results should be consistent with the physical model of underwater imaging, we propose a physics constraint as a feedback controller so that it can guide the estimation of underwater image enhancement. Extensive experiments validate the superiority of the proposed framework. |
| 资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDA19060101] ; Key R&D project of Shandong Province[2019JZZY010102] ; National Natural Science Foundation of China-Shandong Science Foundation[U2006211] ; Key deployment project of Center for Ocean Mega-Science, CAS[COMS2019R02] ; CAS[Y9KY04101L] |
| WOS研究方向 | Engineering ; Oceanography |
| 语种 | 英语 |
| WOS记录号 | WOS:000732190800001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/177533]  |
| 专题 | 中国科学院海洋研究所 |
| 通讯作者 | Li, Xiaofeng |
| 作者单位 | 1.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China 2.Tianjin Univ, Tianjin Int Engn Inst, Tianjin 300072, Peoples R China 3.Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Yuan,Yan, Kangming,Li, Xiaofeng. Underwater Image Enhancement via Physical-Feedback Adversarial Transfer Learning[J]. IEEE JOURNAL OF OCEANIC ENGINEERING,2021:12. |
| APA | Zhou, Yuan,Yan, Kangming,&Li, Xiaofeng.(2021).Underwater Image Enhancement via Physical-Feedback Adversarial Transfer Learning.IEEE JOURNAL OF OCEANIC ENGINEERING,12. |
| MLA | Zhou, Yuan,et al."Underwater Image Enhancement via Physical-Feedback Adversarial Transfer Learning".IEEE JOURNAL OF OCEANIC ENGINEERING (2021):12. |
入库方式： OAI收割
来源：海洋研究所
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