Multi-focus image fusion based on quad-tree decomposition and edge-weighted focus measure

Abstract

Multi-focus image fusion is an important way to obtain all-in-focus images. The goal is to reconstruct all the focused pixels in the source image into the fused image. Generally, in block-based fusion methods, the source image is often decomposed into fixed-size blocks. However, the size of the blocks will affect the fusion quality, and the problems such as blockiness is prone to occur in the fusion results. To this end, we propose a novel multi-focus image fusion method based on optimal block decomposition. Different from the conventional fixed-block-based methods, our method adopts the optimal decomposition of the quad-tree to process the source images. First, a new sum of edge-weighted modified Laplacian (SEWML) is proposed based on the sum of modified Laplacian (SML), which is used as a focus measure to detect the focus information of the source image, and this improved focus measure is more robust than SML. Then, an efficient quad-tree decomposition strategy is proposed to decompose the source images into optimally sized blocks. At the same time, using SEWML to detect the focused blocks in the quad-tree structure of the source images, the focused blocks are naturally combined to form the initial decision map, and the initial decision map is optimized to obtain the final decision map. Finally, the fused image is obtained by the weighted-average rule according to the final decision map. Experimental results show that the proposed method achieves better performance compared to 14 other state-of-the-art multi-focus image fusion methods.