Affiliations: [a] School of International Business, Zhejiang Yuexiu University
| [b] School of Mathematical Sciences, Universiti Sains Malaysia
Correspondence:
[*]
Corresponding author. Ni Chenmin, Associate Professor, School of International Business, Zhejiang Yuexiu University, China. Phd, School of Mathematical Sciences, Universiti Sains Malaysia. E-mail: luckyncm@163.com.
Note: [1] This work was supported by the 2020 scientific research project of Zhejiang Yuexiu University (Project No. D2020004).
Abstract: GPS monitoring systems and the development of driverless vehicles are almost inseparable from camera images. The images taken by traffic cameras often contain certain sky areas and noise, the traditional dark channel prior (DCP) algorithm easily produces color distortion and halo effect, when processing the hazy traffic images with sky and high brightness areas. An optimized Retinex model and dark channel prior algorithm (ORDCP) is proposed in this paper. Firstly by adjusting the calculation method of dark channel image, the proportion of dark channel is improved; Then, the transmittance image is corrected and smoothed by guided filtering and mean filtering. Finally, the Retinex model is fused to save the details.ORDCP corrects the inaccurate calculation of scene transmittance value in DCP algorithm,and modifies some dehazing problems, such as the loss of details, halo effect, contrast and color distortion,etc. Using information entropy (IE) as the objective evaluation index, combined with the subjective evaluation, it is concluded that the algorithm proposed in this paper can effectively retain the detailed information of the image, and eliminate the halo effect. Meanwhile, it meets the visual characteristics of human eyes better, and has some practicality and applicability in traffic control and intelligent detection.
Keywords: Haze removal, traffic image, Retinex model, dark channel prior
