Research on multi-focus image fusion algorithm based on total variation and quad-tree decomposition

Abstract

In order to get the focus region from the two or more source image more effectively and to represent the source image completely and effectively, a decomposition strategy based on the combination of total variation and quad-tree and an improved fusion method of focus region detection are proposed. The theory of total variation and quad-tree is applied to the fusion of multi-focus images. Firstly, two registered experimental images are decomposed into the optimal block decomposition graph by total variation and quad-tree decomposition, respectively. For each block, the initial focus region decision map of the source image is found by using improved Sum-Modified-Laplacian, and the final focus region decision map is obtained by consistency test and morphological processing for the initial focus region decision map. Furthermore, compared with other algorithms, the improved algorithm has more advantages in extracting the focus area, because of improved focus evaluation function and more accurate detection of the focus area. According to the fusion source image of the final focus region decision map, the results of four sets of experiments show that the fusion quality and effect are significantly improved compared with the existing algorithm.