Removing fog from traffic image sequence  

序列交通图像去雾(英文)

作　　者：李楠[1] 路小波[2] 

机构地区：[1]东南大学交通学院,南京210096 [2]东南大学自动化学院,南京210096

出　　处：《Journal of Southeast University(English Edition)》2011年第3期290-294,共5页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China ( No.60972001);the National Key Technologies R& D Program of China during the 11th Five-Year Period ( No. 2009BAG13A06)

摘　　要：Aiming at removing fog from traffic images, a distance field is built according to the characteristics of traffic images, and a novel parameter estimation method based on the traffic image sequence is proposed. The fog model is derived from atmospheric scattering models. The direction of the distance field is parallel to the center line of the road, which increases along a line from the observer to the horizon, and the normalization is carried out to improve the distribution of the distance field model. After parameter initialization, the variations of the average gray values of reference regions are taken as the determining conditions to adjust the parameters. Finally, restorations are made by the fog model. Experimental results show that the proposed method can effectively remove fog from traffic images.为了消除交通图像中的雾,根据交通图像的特点建立了一种距离场并提出了一种新颖的基于序列交通图像的参数估计方法.首先,基于大气散射模型推导出雾模糊模型,将距离场的方向平行于道路中线,距离场沿观察者到地平线的方向递增,并进行归一化处理改善距离场的分布特性;然后,对参数进行初始化,并以参考区域的平均灰度变化作为判定条件,对参数进行调整;最后,根据雾模糊模型实施复原.实验结果表明,所提方法能够对交通图像中的雾进行有效去除.

关 键 词：traffic images FOG distance field depth of field physical model 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]