Multifocus image fusion by combining with mixed-order structure tensors and multiscale neighborhood

• A new fusion scheme based on multiscale neighborhood technique is proposed for multifocus image fusion.
• A new focus measure by combining the structure tensors of mixed order differential is developed.
• An effective focused region detection method is presented based on multiscale neighborhood technique.
• An averaging fusion scheme is presented based on the fusion decision maps of different scale for uncertain regions.
• Our method outperforms the traditional methods in various experiments.

In this study, we propose a new method for multifocus image fusion by combining with the structure tensors of mixed order differentials and the multiscale neighborhood. In this method, the structure tensor of an integral differential is utilized to detect the high frequency regions and the structure tensor of the fractional differential is used to detect the low frequency regions. To improve the performance of the fusion method, we propose a new focus measure based on the multiscale neighborhood technique to generate the initial fusion decision maps by exploiting the advantages of different scales. Next, based on the multiscale neighborhood technique, a post-processing method is used to update the initial fusion decision maps. During the fusion process, the pixels located in the focused inner regions are selected to produce the fused image. In order to avoid discontinuities in the transition zone between the focused and defocused regions, we propose a new “averaging” scheme based on the fusion decision maps at different scales. Our experimental results demonstrate that the proposed method outperformed the conventional multifocus image fusion methods in terms of both their subjective and objective quality.