在尘雾等恶劣天气条件下，由于大气粒子的散射作用，致使获取的道路图像严重退化，给交通运输带来很大的困难。为了提高道路环境的可视性，文中提出了一种基于WLS的雾天交通图像恢复算法。该算法从大气散射模型出发，首先进行大气光照的估计与白平衡处理，然后结合道路环境的约束，构建WLS框架对大气耗散函数进行估计，从而恢复场景反照率。通过实验分析可知，文中算法能够有效去除图像中雾霾，消除了Halo效应的影响，较好地凸显图像远景的细节信息，实现了交通图像的视见度的提高。

Images of roads captured by visual surveillance system are usually degraded by scattering due to atmospheric particles such as haze, fog and mist, which could frequently bring great difficulties to the transportation. In this paper, a novel method was proposed based on weighted least squares(WLS) to remove fog from a single input image. The proposed algorithm begins with estimation of atmospheric light and white balance. Then, through the constraint from the road environment, the weighted least squares(WLS) framework was constructed to estimate atmospheric veil, and restore the fog image by inverting the atmospheric scattering model. The experimental results demonstrates that, compared with the existing algorithm, the proposed method can remove the fog effectively, eliminate the Halo effects, obtain good restoration of distant scene details, and thus realize the improvement of traffic image visibility.