首页|A Naturalness-Preserved Low-Light Enhancement Algorithm for Intelligent Analysis1

A Naturalness-Preserved Low-Light Enhancement Algorithm for Intelligent Analysis1

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Images/videos captured in low-light conditions often present low luminance and contrast. Although the existing low-light enhancement algorithms can improve the subjective perception, color distortion and over-enhancement are extremely obvious, which will disturb the subsequent intelligent analysis. Therefore, a naturalness-preserved low-light enhancement algorithm for intelligent analysis is proposed in this paper. An enhancement model is established in RGB color space. Images of ColorChecker color chart are captured under a series of light conditions. To preserve the naturalness, the factors of the proposed enhancement model are estimated by the images captured in practical illumination environment. Experimental results demonstrate that the proposed algorithm can produce natural enhanced results and improve the performance of vehicle license plate localization and skin color detection compared to the existing algorithms. Furthermore, the proposed algorithm can process the 720p videos at the speed of 28.3 fps on average.

Low-light enhancementIntelligent analysisNaturalness-preservedVehicle license plate localizationsSkin color detection

ZHUO Li、HU Xiaochen、LI Jiafeng、ZHANG Jing、LI Xiaoguang

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Signal & Information Processing Laboratory, Beijing University of Technology, Beijing 100124, China

Collaborative Innovation Center of Electric Vehicles, Beijing 100124, China

work in this paper is supported by the National Natural Science Foundation of Chinawork in this paper is supported by the National Natural Science Foundation of Chinawork in this paper is supported by the National Natural Science Foundation of Chinawork in this paper is supported by the National Natural Science Foundation of ChinaImportation and Development of High-Calibre Talents Project of Beijing Municipal InstitutionsImportation and Development of High-Calibre Talents Project of Beijing Municipal InstitutionsBeijing Natural Science FoundationBeijing Natural Science FoundationScience and Technology Development Program of Beijing Education CommitteeScience and Technology Development Program of Beijing Education CommitteeFunding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Mu

61531006No.61372149No.61370189No.61471013CIT&TCD20150311No.CIT&TCD2014040434142009No.4163071KM201410005002No.KM201510005004

2019

10.1049/cje.2018.12.004

中国电子杂志(英文版)

中国电子杂志(英文版)

CSTPCDCSCDSCIEI
ISSN：1022-4653
年,卷(期)：2019.28(2)
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