Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation Surveillance

doi:10.1109/TITS.2024.3359755

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation Surveillance
	Qu, Jingxiang 1,2; Liu, Ryan Wen 1,2; Gao, Yuan 1,2; Guo, Yu 2; Zhu, Fenghua 3; Wang, Fei-Yue 3
刊名	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
	2024-02-07
页码	13
关键词	Intelligent transportation system (ITS) transportation surveillance low-light image enhancement ultra-high-definition (UHD) double domain guidance
ISSN号	1524-9050
DOI	10.1109/TITS.2024.3359755
通讯作者	Liu, Ryan Wen(wenliu@whut.edu.cn)
英文摘要	Real-time transportation surveillance is an essential part of the intelligent transportation system (ITS). However, images captured under low-light conditions often suffer poor visibility with types of degradation, such as noise interference and vague edge features, etc. With the development of imaging devices, the quality of the visual surveillance data is continually increasing, like 2K and 4K, which have more strict requirements on the efficiency of image processing. To satisfy the requirements on both enhancement quality and computational speed, this paper proposes a double domain guided real-time low-light image enhancement network (DDNet) for ultra-high-definition (UHD) transportation surveillance. Specifically, we design an encoder-decoder structure as the main architecture of the learning network. In particular, the enhancement processing is divided into two subtasks (i.e., color enhancement and gradient enhancement) via the proposed coarse enhancement module (CEM) and LoG-based gradient enhancement module (GEM), which are embedded in the encoder-decoder structure. It enables the network to enhance the color and edge features simultaneously. Through the decomposition and reconstruction on both color and gradient domains, our DDNet can restore the detailed feature information concealed by the darkness with better visual quality and efficiency. The evaluation experiments on standard and transportation-related datasets demonstrate that our DDNet provides superior enhancement quality and efficiency compared with state-of-the-art methods. Besides, the object detection and scene segmentation experiments indicate the practical benefits for higher-level image analysis under low-light environments in ITS. The source code is available at https://github.com/QuJX/DDNet.
资助项目	National Key Research and Development Program of China
WOS关键词	SIGNAL FIDELITY ; DEEP NETWORK
WOS研究方向	Engineering ; Transportation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001161083400001
资助机构	National Key Research and Development Program of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/57763]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Liu, Ryan Wen
作者单位	1.Wuhan Univ Technol, Sch Nav, Wuhan 430063, Peoples R China 2.Wuhan Univ Technol, State Key Lab Maritime Technol & Safety, Wuhan, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Qu, Jingxiang,Liu, Ryan Wen,Gao, Yuan,et al. Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation Surveillance[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2024:13.
APA	Qu, Jingxiang,Liu, Ryan Wen,Gao, Yuan,Guo, Yu,Zhu, Fenghua,&Wang, Fei-Yue.(2024).Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation Surveillance.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,13.
MLA	Qu, Jingxiang,et al."Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation Surveillance".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2024):13.