A New Pixel-Level Multi-focus Image Fusion Algorithm
一种新的像素级多聚焦图像融合算法

A new fusion method for fusing two spatially registered multi-focus images is proposed in this paper.It is based on multi-resolution wavelet decomposition,Self-Organizing Feature Map(SOFM) neural networks and Evolutionary Strategies(ES).First,a normalized feature image,which represents the local region clarity difference of two source images,is extracted by redundant wavelet transform,then the feature image is clustered by SOFM learning algorithm and every pixel pair in source images is classified into a certain class which indicates different clarity differences.Finally,to each pixel pair in different classes,different fusion factors are used to fuse it;these fusion factors are determined by evolution strategies to achieve the best fusion performance.Experimental results show that the proposed method outperforms the Laplace transform and wavelet transform methods.