Several tiny particulates are suspended in air during poor weather condition (e.g.

haze or fog). The color and contrast of the captured picture from image devices are severely degraded because of scattering

thereby consequently affecting visual experience. Haze is a common phenomenon in China's cities and towns

especially in the metropolis. Haze reduces visibility and seriously affects the closed circuit television surveillance system

thereby leading to difficulties in traffic monitoring and increase in traffic accidents. Using traditional methods to remove the haze in traffic image usually results in various problems

such as halo and color distortion. To remove the large sky area in traffic image

this paper proposes a haze-removal method based on sky segmentation and dark channel prior. We use the maximum of the top 0.1 percent brightest pixels in the dark channel (as selected by He) corresponding to the original image as the value to the atmospheric extinction coefficient . Accordingly

the value of in each channel can be made closer to the maximum pixel value of 255. Resulting image after haze removal can generate color cast or a large number of color spots. According to the characteristics of haze image in traffic scene

we propose a novel algorithm that automatically separates sky regions to optimize the model and thus solve the image distortion of sky region after dehazing by dark channel prior. For sky segmentation

we introduce an OTSU method to complete the task. In our haze removal algorithm

we present the average intensity of the sky as the atmospheric extinction coefficient

and estimate the scene transmission of the sky and the non-sky regions. Then

we combine two parts of the scene transmission as a whole to refine the transmission. This step makes the restored sky region highly natural looking. Compared with Fattal's method and He's method

our algorithm obtains better image sharpness and edge details of the recovered image. The distortion of the recovered image is also lower than that of the recovered image by Fattal's method and He's method. In the rehabilitation of the sky region

our algorithm exhibits highly natural looking and smooth resulting image. On the contrary

Fattal's method and He's method show a large number of spots or halos appearing in the sky region. The proposed method can effectively restore the traffic haze image. Specifically

no spots or distortions are found in the sky region of the recovered image by our method. The proposed method can also provide an effective theoretical basis and technical support for road traffic supervision. However

our algorithm has the following limitations. The effect of the recovered image is poor when distant objects are under a thick-haze environment. This result is due to the optimization of scene transmittance. The attenuation coefficient of the transmittance (scattering coefficient) is assumed to be constant. However

in the actual atmosphere

the scattering coefficient changes under different weather conditions. Therefore

our future research will incorporate the weather factor into our algorithm to optimize an accurate and robust approach for removing the haze from traffic scene image.