基于双子代差分演化和自适应分块机制的多聚焦图像融合算法

Multi-focus Image Fusion Based on Twin-generation Differential Evolution and Adaptive Block Mechanism

基于分块的多聚焦图像融合算法是多聚焦图像融合领域中的一个重要算法。基于差分演化的多聚焦图像融合算法将图像分块大小作为差分演化算法的种群,通过多次演化,最后获得使融合图像效果最好的图像分块。为克服标准差分演化算法由于丢失父代种群的部分信息导致收敛速度变慢、全局搜索范围较小,以及当对应图像块的清晰度相等时该算法的处理方式会改变源图像的像素值的缺点,在原算法的基础上,引入双子代机制和自适应分块机制,提出一种基于双子代差分演化和自适应分块机制的多聚焦图像融合算法。在演化过程中生成两个子代种群,最大程度上保留父代种群的信息,扩大全局搜索范围,提高算法的收敛性能;利用自适应分块机制,当出现图像块清晰度相等的情况时,将图像块分解成更小的图像块,然后再进行清晰度的比较,使改进算法获得的融合图像比原算法获得的效果更好,而且不会改变源图像的像素值。实验结果表明,基于双子代差分演化和自适应分块机制的多聚焦图像融合算法可以获得比原算法效果更好的融合图像,而且收敛性能更好。

Multi-focus image fusion algorithm based on block is an important algorithm in the field of image fusion. Multi-focus image fusion algorithm based on differential evolution takes the image block size as the population of differential evolution algorithm, after many evolutions, finally getting the image block with the best fusion image effect. In order to overcome the shortcomings that the standard algorithm will lose part of the information of parent population and result in slow convergence and smaller range of global search, and when the image resolution of the corresponding blocks are same,it will change the pixels of the source images,on the basis of the multi-focus image fusion algorithm which is based on differential evolution algorithm, a new fusion algorithm was proposed by introducing the twin-genera- tion mechanism and adaptive block mechanisnx This algorithm generates two progeny populations during evolution, keeps the information of parent population to the greatest extent, expands the global search range and improves the con- vergence performance. When the image resolution of the corresponding blocks are the same, it cuts the image block into smaller blocks and compars their resolution, then gets a better fused image and will not change the pixel of the source images. Experimental results show that the improved algorithm can get a better fused image than the former algorithm and has better convergence performance.