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Journal of Sensors
Volume 2015, Article ID 516326, 9 pages
Research Article

A Novel Method of Adaptive Traffic Image Enhancement for Complex Environments

Cao Liu,1,2 Hong Zheng,1,2 Dian Yu,1,2 and Xiaohang Xu1,2

1School of Electronic Information, Wuhan University, 129 Luoyu Road, Wuhan, Hubei 430072, China
2Hubei Research and Development Center of Vision Perception and Intelligent Transportation Technology, Wuhan, Hubei 430072, China

Received 16 January 2015; Accepted 15 April 2015

Academic Editor: Pietro Siciliano

Copyright © 2015 Cao Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


There exist two main drawbacks for traffic images in classic image enhancement methods. First is the performance degradation that occurs under frontlight, backlight, and extremely dark conditions. The second drawback is complicated manual settings, such as transform functions and multiple parameter selection mechanisms. Thus, this paper proposes an effective and adaptive parameter optimization enhancement algorithm based on adaptive brightness baseline drift (ABBD) for color traffic images under different luminance conditions. This method consists of two parts: brightness baseline model acquisition and adaptive color image compensation. The brightness baseline model can be attained by analyzing changes in light along a timeline. The adaptive color image compensation involves color space remapping and adaptive compensation specific color components. Our experiments were tested on various traffic images under frontlight, backlight, and during nighttime. The experimental results show that the proposed method achieved better effects compared with other available methods under different luminance conditions, which also effectively reduced the influence of the weather.