为了解决机动车尾气监测系统中所监测尾气红外图像的配准问题,提出了基于双重尺度搜索遗传算法的图像配准方法。该方法以互信息为相似性度量,对传统遗传算法的交叉变异概率公式进行调整,利用所提出的双重尺度搜索遗传算法作为优化算法,实现了机动车尾气红外图像的高精度配准。采用该方法进行配准实验得到的横向平移量、纵向平移量和旋转角的均方根误差分别为0.0949、0.0447和0.0000,优于其他方法。相对于基于自适应遗传算法和蚁群算法的图像配准方法,该方法精度更高,稳定性更好;相对于Powell算法,该方法抗噪声能力更强,更适合尾气图像配准,为后续污染气体浓度反演与计算打下了良好的基础。

In order to solve the infrared image registration problem of the monitored exhaust in vehicle exhaust monitoring system, a method of infrared image registration based on double scale search genetic algorithm is proposed. The method takes mutual information as the similarity measure and modify probability formula of crossover and mutation in the traditional genetic algorithm. The proposed double scale search genetic algorithm is used as the optimization algorithm to realize high precision registration of vehicle exhaust infrared image. The obtained root-mean-square error of horizontal translation, vertical translation and rotation angle are 0.0949, 0.0447 and 0.0000, respectively, and the results of experiment with this method are better than other methods and it proves the effectiveness of the proposed method. In comparison to the image registration method based on the adaptive genetic algorithm and ant colony algorithm, and the proposed method has higher precision and better stability. Compared with the Powell algorithm, the proposed method has stronger anti-noise ability and is more suitable for exhaust image registration, which is a good foundation for inversion and calculation of pollution gas concentration.