基于综合性能最优的小波域图像融合新方法

New method on image fusion based on optimal comprehensive performance via wavelet transform

以提高融合图像的综合性能为目标,提出了一种基于综合性能最优的小波域图像融合方法。首先根据融合目的和专家经验,选择融合图像的多个客观评价标准,并由之构建综合性能指标。然后,对待融合图像分别进行多尺度小波分解,引入遗传算法创建初始种群作为小波域的图像融合参数,求解融合图像及其综合性能值;利用综合性能评价指标最优作为遗传算法的适应度函数,经过选择、交叉、变异等种群进化操作,快速逼近并定位最优性能参数,最后得到综合性能最优的融合图像,即该参数对应的融合图像。实验结果显示,所提融合方法的综合融合效果优于常见的小波变换、金字塔分解等多种融合方法。

In order to improve the comprehensive fusion effect,a method on image fusion based on the optimal comprehensive performance via wavelet transform is presented.First,according to the fusion purpose and experts’experience,some objective evaluation standards of fusion are selected and used to construct a comprehensive evaluation model.Second,each source image is decomposed by wavelet transform with the fusion parameters generated from the initialization of Genetic Algorithm（GA）,and then several fusion images and their comprehensive performance values are computed.Next,guided by the fitness function representing the optimal performance,GA approaches the best performance parameters generation by generation via operations such as selec- tion,crossover and mutation.After the optimal parameter is located,the best fusion image can be obtained by the corresponding wavelet reconstruction.Finally,experiment results show that this method is superior to many existing fusion schemes.