Multi-focus image fusion based on guided filter and mixed focus measure

Abstract

The multi-focus image fusion technique is to extract the focus regions from source images and compose them together to form a clear image in the full field of view. In order to further improve the accuracy of focus region detection and ensure its efficiency, a novel multi-focus image fusion method in spatial domain, based on guided filter and mixed focus measure, is proposed in this paper. Firstly, a guided filter is employed as an edge-preserving smoothing operator to process the source images, and the difference operator is used between the filtered images and the source images to extract salient feature. Subsequently, the salient feature maps are measured by the mixed focus measure, combining the sum of energy of edge (SEOE) and the sum of local variance (SLV), to detect the focus regions, and the initial decision map is obtained. For holes of different sizes in the initial decision map, the closing operation and the small area removal strategy are used to fill and connect the truncated regions, and then the opening operation and the guide filter are used to optimize the decision map boundary to obtain the final decision map. Finally, the multi-focus fusion image is obtained by the pixel-wise weighted-averaging rule according to the final decision map. Simulation results demonstrate that the method is superior to some existing fusion methods on both subjective visual perception and objective evaluation metrics.