  Multi-focus image fusion (MFF) is a popular technique to generate an
all-in-focus image, where all objects in the scene are sharp. However, existing
methods pay little attention to defocus spread effects of the real-world
multi-focus images. Consequently, most of the methods perform badly in the
areas near focus map boundaries. According to the idea that each local region
in the fused image should be similar to the sharpest one among source images,
this paper presents an optimization-based approach to reduce defocus spread
effects. Firstly, a new MFF assessmentmetric is presented by combining the
principle of structure similarity and detected focus maps. Then, MFF problem is
cast into maximizing this metric. The optimization is solved by gradient
ascent. Experiments conducted on the real-world dataset verify superiority of
the proposed model. The codes are available at
https://github.com/xsxjtu/MFF-SSIM.
