FHSI and QRCPE-Based Low-Light Enhancement With Application to Night Traffic Monitoring Images

This paper proposes a fast HSI (hue, saturation, intensity) color space and an orthogonal triangular with column pivoting (QRCP) enhancement model to tackle the large size RGB (red, green, blue) night traffic monitoring (NTM) image. Firstly, the fast HSI (FHSI) is proposed to decompose the light and...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2024-07, Vol.25 (7), p.6978-6993
Main Authors: Hu, Changhui, Yi, Weilin, Hu, Kerui, Guo, Yanyong, Jing, Xiaoyuan, Liu, Pan
Format: Article
Language:English
Subjects:
Angles (geometry)
Color
fast HSI color space
Image color analysis
Image enhancement
Learning systems
Light
Lighting
Low-light image enhancement
Luminous intensity
Mathematical models
Monitoring
Night
night traffic monitoring image
orthogonal triangular with column pivoting enhancement
Reflectivity
Saturation (color)
Triangles
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Summary:This paper proposes a fast HSI (hue, saturation, intensity) color space and an orthogonal triangular with column pivoting (QRCP) enhancement model to tackle the large size RGB (red, green, blue) night traffic monitoring (NTM) image. Firstly, the fast HSI (FHSI) is proposed to decompose the light and color information of the RGB image, whose hue is defined as the cosine value of the included angle, instead of the included angle in HSI. The saturation of FHSI is defined as the ratio of the projection vector length and the side length of the projection equilateral triangle, and a saturation correction model is further proposed to correct color distortion of the low-light image by adjusting the saturation of FHSI. FHSI is more concise and faster than HSI. Secondly, a novel QRCP enhancement (QRCPE) model is proposed to improve the light of the low-light image by enhancing the intensity of FHSI, which first strengthens diagonal elements of QRCP, and followed by controlling the normalization of strengthened diagonal elements of QRCP. Finally, the FHSI-QRCPE based RGB image can be obtained by transforming the processed FHSI to RGB. The experimental results on NTM, SICE, ExDark, and BDD 100K databases, indicate that the proposed FHSI-QRCPE is fast and efficient to tackle low-light image enhancement.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2023.3342799