Quadtree-based multi-focus image fusion using a weighted focus-measure

The purpose of multi-focus image fusion is integrating the partially focused images into one single image which is focused everywhere. To achieve this purpose, we propose a new quadtree-based algorithm for multi-focus image fusion. In this work, an effective quadtree decomposition strategy is presented. According to the proposed decomposition strategy, the source images are decomposed into blocks with optimal sizes in a quadtree structure. And in this tree structure, the focused regions are detected by using a new weighted focus-measure, named as the sum of the weighted modified Laplacian. Finally, the focused regions could be well extracted from the source images and reconstructed to produce one fully focused image. Moreover, the new weighted focus-measure performs better than the commonly used focus-measures on the detection of the focused regions, since it is sensitive to the homogeneous regions. The proposed algorithm is simple yet effective, because of the quadtree decomposition strategy and the new weighted focus-measure. To do the comparison, the proposed algorithm is compared with several existing fusion algorithms, in both the qualitative and quantitative ways. The experimental results show that the proposed algorithm yields good results.