结合天空分割和局部透射率优化交通图像去雾算法

针对现有的去雾算法在处理交通场景图像时由于透射率估计与实际情况偏差较大，尤其交通图像含有天空区域时容易导致色彩失真和产生光晕效应等问题，在暗原色先验理论的基础上，提出一种结合天空分割和局部透射率优化的交通图像快速去雾算法。首先，采用大津算法(OTSU)将原始图像分割为天空区域与非天空区域；其次，对非天空区域，利用最大值滤波和引导滤波对其透射率进行优化，采用自适应参数调整的方法对天空区域的透射率进行修正；最后，对复原的图像利用限制对比度自适应直方图均衡法(Contrast Limited Adaptive Histogram Equalization, CLAHE)调整色调，提高亮度。实验结果表明，对于天空区域，本文算法不但能有效减少产生颜色失真和光晕效应的现象，得到更为自然清晰的复原结果，对于非天空区域，复原结果的清晰度和对比度更高，而且，算法保持较高的运行效率，另外，去雾后的图像在方差、平均梯度、信息熵等指标上相对于暗原色先验算法、Tarel算法、Meng算法、Zhu算法和Berman算法均有所提升。本文方法可较好地复原雾天交通图像，能为雾天模糊的交通图像快速有效去雾复原提供重要有益的理论基础和技术支持。

A Single Traffic Image Fast Dehazing Method with Sky Segmentation #br# and Local Transmittance Optimization

Traffic photographs taken in hazy weather are degraded, due to the suspended particles in the air and scatter light. Since the scattered environment light is mixed into the light accepted by the observer, the contrast and sharpness of the hazy traffic image decrease, and the difficulties of subsequent processing and analysis increase. These problems, directly influence the full play of the circuit television surveillance system utility. Therefore, fast and effective traffic image dehazing has important application value. In the existing dehazing algorithm, the transmittance estimation deviates quite greatly from the actual situation. Especially when dealing with the sky area, it is easily lead to problems such as color distortion and halo effect. On the basis of the dark channel prior theory, this paper puts forward a fast haze removal method integrating sky segmentation with local transmittance optimization. First, the original traffic image is segmented into sky area and non-sky area by OTSU. Secondly, on the basis of the dark channel prior, the transmittance of the non-sky area is optimized by the maximum filtering and guided filtering, and the transmittance of the sky area is corrected by adaptive parameter adjustment method. In the end, the restored image is adjusted by Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve the image brightness. Experimental results show that the proposed algorithm can effectively reduce the phenomenon of color distortion and halo effect in the sky area, and obtain a more natural and clear restored result. For the non-sky area, the clarity and contrast of the restored result are higher, and besides, the proposed algorithm keeps high efficiency. What’s more, compared with the dark channel prior algorithm, Tarel algorithm, Meng algorithm, Zhu algorithm and Berman algorithm, the proposed algorithm does better in terms of variance, average gradient, image information entropy and other indicators. This proposed algorithm can effectively and quickly restore the haze traffic image and reduce the color distortion and halo effect in the sky area. The restored image has good clarity and color revivification degree, and obtains better image sharpness and contrast enhancement. The proposed algorithm can provide good theoretical and technical support for the road traffic supervision.