Low-Illumination Road Image Enhancement by Fusing Retinex Theory and Histogram Equalization

Han, Yi, Chen, Xiangyong, Zhong, Yi, Huang, Yanqing, Li, Zhuo, Han, Ping, Li, Qing and Yuan, Zhenhui (2023) Low-Illumination Road Image Enhancement by Fusing Retinex Theory and Histogram Equalization. Electronics, 12 (4). p. 990. ISSN 2079-9292

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Official URL: https://doi.org/10.3390/electronics12040990


Low-illumination image enhancement can provide more information than the original image in low-light scenarios, e.g., nighttime driving. Traditional deep-learning-based image enhancement algorithms struggle to balance the performance between the overall illumination enhancement and local edge details, due to limitations of time and computational cost. This paper proposes a histogram equalization–multiscale Retinex combination approach (HE-MSR-COM) that aims at solving the blur edge problem of HE and the uncertainty in selecting parameters for image illumination enhancement in MSR. The enhanced illumination information is extracted from the low-frequency component in the HE-enhanced image, and the enhanced edge information is obtained from the high-frequency component in the MSR-enhanced image. By designing adaptive fusion weights of HE and MSR, the proposed method effectively combines enhanced illumination and edge information. The experimental results show that HE-MSR-COM improves the image quality by 23.95% and 10.6% in two datasets, respectively, compared with HE, contrast-limited adaptive histogram equalization (CLAHE), MSR, and gamma correction (GC).

Item Type: Article
Additional Information: Funding information: This work was supported by a grant from the National Natural Science Foundation of China (Grant No. 61801341). This work was also supported by the Research Project of Wuhan University of Technology Chongqing Research Institute (No. YF2021-06).
Uncontrolled Keywords: low illumination; image enhancement; Retinex theory; histogram equalization; image fusion
Subjects: G400 Computer Science
G500 Information Systems
G600 Software Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 27 Feb 2023 15:20
Last Modified: 27 Feb 2023 15:30
URI: https://nrl.northumbria.ac.uk/id/eprint/51500

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