基于改进自适应遗传算法的图像配准方法

采用遗传算法进行图像配准时,存在收敛速度慢、易早熟的问题,可能导致误配。为克服这些缺点，提出了改进的自适应遗传算法(improved adaptive genetic algorithm，IAGA)。该算法以互信息作为相似性测度，通过对遗传参数设置的改进，自适应的调解进化过程中的交叉概率和变异概率，既提高了遗传算法的收敛速度,又有效地防止了早熟。实验结果表明,改进算法具有更好的有效性和精确性。

Image registration based on improved adaptive genetic algorithm

The usage of genetic algorithm in image registration has some shortcomings,such as slow convergence and premature,which will result in artifacts.To overcome these problems,an improved adaptive genetic algorithm(IAGA)was proposed.The algorithm regard mutual information as similarity measure,improve the setting of genetic parameter,adjust adaptively the probabilities of crossover and mutation during the evolutionary process.The convergence rate was accelerated and premature was avoided.Experimental results indicate that improved genetic algorithm can achieve better feasibility and accuracy.