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Low-Light Image Enhancement via a Deep Hybrid Network

文献类型:期刊论文

作者Ren, Wenqi7; Yang, Ming-Hsuan1; Du, Junping2; Cao, Xiaochun7; Xu, Xiangyu3; Xu, Qianqian4; Ma, Lin5; Liu, Sifei6
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2019-09-01
卷号28期号:9页码:4364-4375
ISSN号1057-7149
关键词Low-light image enhancement convolutional neural network recurrent neural network
DOI10.1109/TIP.2019.2910412
英文摘要Camera sensors often fail to capture clear images or videos in a poorly lit environment. In this paper, we propose a trainable hybrid network to enhance the visibility of such degraded images. The proposed network consists of two distinct streams to simultaneously learn the global content and the salient structures of the clear image in a unified network. More specifically, the content stream estimates the global content of the low-light input through an encoder-decoder network. However, the encoder in the content stream tends to lose some structure details. To remedy this, we propose a novel spatially variant recurrent neural network (RNN) as an edge stream to model edge details, with the guidance of another auto-encoder. The experimental results show that the proposed network favorably performs against the state-of-the-art low-light image enhancement algorithms.
资助项目National Natural Science Foundation of China[U1736219] ; National Natural Science Foundation of China[U1605252] ; National Natural Science Foundation of China[U1803264] ; National Natural Science Foundation of China[61532006] ; National Natural Science Foundation of China[61772083] ; National Natural Science Foundation of China[61802403] ; National Key R&D Program of China[2018YFB0803701] ; Beijing Natural Science Foundation[L182057] ; CCF-Tencent 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:000473641100014
源URL[http://119.78.100.204/handle/2XEOYT63/4315]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Cao, Xiaochun
作者单位1.Univ Calif Merced, Sch Engn, Merced, CA 95343 USA
2.Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China
3.SenseTime, Beijing 100084, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100080, Peoples R China
5.Tencent AI Lab, Shenzhen 518000, Peoples R China
6.NVIDIA Res, Santa Clara, CA 95051 USA
7.Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur, Beijing 100093, Peoples R China
推荐引用方式
GB/T 7714
Ren, Wenqi,Yang, Ming-Hsuan,Du, Junping,et al. Low-Light Image Enhancement via a Deep Hybrid Network[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2019,28(9):4364-4375.
APA Ren, Wenqi.,Yang, Ming-Hsuan.,Du, Junping.,Cao, Xiaochun.,Xu, Xiangyu.,...&Liu, Sifei.(2019).Low-Light Image Enhancement via a Deep Hybrid Network.IEEE TRANSACTIONS ON IMAGE PROCESSING,28(9),4364-4375.
MLA Ren, Wenqi,et al."Low-Light Image Enhancement via a Deep Hybrid Network".IEEE TRANSACTIONS ON IMAGE PROCESSING 28.9(2019):4364-4375.

入库方式: OAI收割

来源:计算技术研究所

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