基于图像分割的交通图像快速去雾算法

现有去雾算法较少考虑交通图像的特征，直接应用于交通图像去雾效果和实时性较差。针对这一情况，在充分分析了雾天交通图像特征的基础上，提出一种基于图像分割的交通图像快速去雾算法。算法首先采用改进的均值漂移算法分割出天空区域，然后在天空区域中较准确的估计出大气光强度值，最后采用基于双边滤波器的改进暗原色先验（DCP）算法实现去雾，并结合雾天交通图像的特征对去雾图像进行了后处理，增强了去雾效果。实验结果证明该算法实时性高且去雾效果好，在交通图像去雾方面所提出的算法的综合性能优于现有的同类去雾算法。

Fast Traffic Image Defogging Algorithm Based on Image Segmentation

Less the traffic image features were considered in the existing defogging algorithms. Defogging quality and real-time performance are poor if they are directly applied to defog. Having analyzed the traffic image features, a fast traffic image defogging algorithm was proposed based on image segmentation technique. Firstly, an improved mean shift algorithm was used to segment sky region. Then atmospheric light intensity was accurately estimated in the sky region. Finally the improved dark channel prior (DCP) algorithm based on bilateral filter was used to defog to the traffic image, and post-processing was done to these defogging image by using fogging image features to enhance the defogging quality. The experimental results demonstrate that the proposed algorithm has higher real-time performance and can obtain better defogging quality. The overall performance of the proposed algorithm is better than that of existing similar algorithm in defogging for traffic images.