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PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators

文献类型：期刊论文

作者Cong, Runmin2,3,4; Yang, Wenyu2,5; Zhang, Wei3,4; Li, Chongyi6; Guo, Chun-Le6; Huang, Qingming1,7,8; Kwong, Sam9,10
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2023
卷号32页码:4472-4485
ISSN号1057-7149
关键词Underwater image enhancement generative adversarial network physical model degradation quantization
DOI10.1109/TIP.2023.3286263
英文摘要Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we propose a physical model-guided GAN model for UIE in this paper, referred to as PUGAN. The entire network is under the GAN architecture. On the one hand, we design a Parameters Estimation subnetwork (Par-subnet) to learn the parameters for physical model inversion, and use the generated color enhancement image as auxiliary information for the Two-Stream Interaction Enhancement sub-network (TSIE-subnet). Meanwhile, we design a Degradation Quantization (DQ) module in TSIE-subnet to quantize scene degradation, thereby achieving reinforcing enhancement of key regions. On the other hand, we design the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results. Extensive experiments on three benchmark datasets demonstrate that our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics. The code and results can be found from the link of https://rmcong.github.io/proj_PUGAN.html.
资助项目National Key Research and Development Program of China[2021ZD0112100] ; National Natural Science Foundation of China[62002014] ; National Natural Science Foundation of China[61991411] ; National Natural Science Foundation of China[U1913204] ; National Natural Science Foundation of China[62236008] ; National Natural Science Foundation of China[U21B2038] ; National Natural Science Foundation of China[61931008] ; Taishan Scholar Project of Shandong Province[tsqn202306079] ; Natural Science Foundation of Shandong Province for Distinguished Young Scholars[ZR2020JQ29] ; HongKong Innovation and Technology Commission (InnoHK Project CIMDA) ; Hong Kong GRF-RGC General Research Fund[11209819] ; Hong Kong GRF-RGC General Research Fund[CityU 9042816] ; Hong Kong GRF-RGC General Research Fund[11203820 (9042598)] ; Young Elite Scientist Sponsorship Program by the China Association for Science and Technology[2020QNRC001] ; CAAI-Huawei Mind Spore Open Fund
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001045271200006
源URL[http://119.78.100.204/handle/2XEOYT63/21323]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhang, Wei; Li, Chongyi
作者单位1.Peng Cheng Lab, Shenzhen 518055, Peoples R China
2.Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China
3.Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Peoples R China
4.Minist Educ, Key Lab Machine Intelligence & Syst Control, Jinan 250061, Peoples R China
5.Beijing Key Lab Adv Informat Sci & Network Technol, Beijing 100044, Peoples R China
6.Nankai Univ, Coll Comp Sci, Tianjin, Peoples R China
7.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China
8.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
9.City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China
10.City Univ Hong Kong, Shenzhen Res Inst, Shenzhen 518000, Peoples R China
推荐引用方式
GB/T 7714		 Cong, Runmin,Yang, Wenyu,Zhang, Wei,et al. PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2023,32:4472-4485.
APA Cong, Runmin.,Yang, Wenyu.,Zhang, Wei.,Li, Chongyi.,Guo, Chun-Le.,...&Kwong, Sam.(2023).PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators.IEEE TRANSACTIONS ON IMAGE PROCESSING,32,4472-4485.
MLA Cong, Runmin,et al."PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators".IEEE TRANSACTIONS ON IMAGE PROCESSING 32(2023):4472-4485.

入库方式： OAI收割

来源：计算技术研究所

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