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

Content Provider	IET Digital Library
Author	Zhuo, Li Hu, Xiaochen Li, Jiafeng Zhang, Jing Li, Xiaoguang
Abstract	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.
Starting Page	316
Ending Page	324
Page Count	9
ISSN	10224653
Volume Number	28
e-ISSN	20755597
Issue Number	Issue 2, Mar (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/28/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2018.12.004
Journal	Chinese Journal of Electronics
Publisher Date	2019-03-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	Computer Vision And Image Processing Technique Contrast Enhancement Model Existing Low-light Enhancement Algorithm Image Colour Analysis Image Enhancement Image Sonsor Lighting Low Luminance Low-light Condition Natural Enhanced Result Naturalness-preserved Low-light Enhancement Algorithm Optical, Image And Video Signal Processing Subsequent Intelligent Analysis Traffic Engineering Computing
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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