ZHUO Li, HU Xiaochen, LI Jiafeng, ZHANG Jing, LI Xiaoguang. A Naturalness-Preserved Low-Light Enhancement Algorithm for Intelligent Analysis[J]. Chinese Journal of Electronics, 2019, 28(2): 316-324. doi: 10.1049/cje.2018.12.004
Citation: ZHUO Li, HU Xiaochen, LI Jiafeng, ZHANG Jing, LI Xiaoguang. A Naturalness-Preserved Low-Light Enhancement Algorithm for Intelligent Analysis[J]. Chinese Journal of Electronics, 2019, 28(2): 316-324. doi: 10.1049/cje.2018.12.004

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

doi: 10.1049/cje.2018.12.004
Funds:  The work in this paper is supported by the National Natural Science Foundation of China (No.61531006, No.61372149, No.61370189, No.61471013), the Importation and Development of High-Calibre Talents Project of Beijing Municipal Institutions (No.CIT&TCD20150311, No.CIT&TCD201404043), the Beijing Natural Science Foundation (No.4142009, No.4163071), the Science and Technology Development Program of Beijing Education Committee (No.KM201410005002, No.KM201510005004), and Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality.
  • Received Date: 2016-12-09
  • Rev Recd Date: 2017-11-30
  • Publish Date: 2019-03-10
  • 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.
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