Multi-focus image fusion using image-partition-based focus detection

Focus detection based fusion algorithm is a vital alternative in multi-focus image fusion applications. In this kind of fusion algorithms, focus detection measure is a key factor. However, nearly all of them tend to make incorrect predictions in the smooth regions which are close to edges and textures, because these regions are affected by edges and textures and intensities become quite different if they are blurred. In this paper, we propose a new focus detection based multi-focus image fusion algorithm. First of all, the source images are partitioned into three parts: edges, textures, and smooth regions. Pixels in smooth regions are further classified into two catalogues according to their distances from edges or textures. Then, we formulate a new focus detection rule in which pixels in smooth parts are treated differently according to their classification. Finally, the fused image is achieved with the assistance of fusing map. The interests of algorithm lie in its ability of improving the accuracy of focus detection and eliminating blockiness in fused images. Experimental results have shown that the proposed fusion algorithm retains good ratings by Human Visual System (HVS) and objective measures compared to other multi-focus fusion algorithms.