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

doi:10.1109/TIP.2023.3286263

	PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators
	Cong, Runmin 1,2,3; Yang, Wenyu 1,4; Zhang, Wei 2,3; Li, Chongyi 5; Guo, Chun-Le 5; Huang, Qingming 6,7,8; Kwong, Sam 9,10
	2023
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	32 页码:4472-4485
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.
关键词	Underwater image enhancement generative adversarial network physical model degradation quantization
DOI	10.1109/TIP.2023.3286263
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:001045271200006
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21324
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.Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China 2.Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Peoples R China 3.Minist Educ, Key Lab Machine Intelligence & Syst Control, Jinan 250061, Peoples R China 4.Beijing Key Lab Adv Informat Sci & Network Technol, Beijing 100044, Peoples R China 5.Nankai Univ, Coll Comp Sci, Tianjin, Peoples R China 6.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China 7.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 8.Peng Cheng Lab, Shenzhen 518055, 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.