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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 |
DOI | 10.1109/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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