  Capturing an all-in-focus image with a single camera is difficult since the
depth of field of the camera is usually limited. An alternative method to
obtain the all-in-focus image is to fuse several images focusing at different
depths. However, existing multi-focus image fusion methods cannot obtain clear
results for areas near the focused/defocused boundary (FDB). In this paper, a
novel {\alpha}-matte boundary defocus model is proposed to generate realistic
training data with the defocus spread effect precisely modeled, especially for
areas near the FDB. Based on this {\alpha}-matte defocus model and the
generated data, a cascaded boundary aware convolutional network termed MMF-Net
is proposed and trained, aiming to achieve clearer fusion results around the
FDB. More specifically, the MMF-Net consists of two cascaded sub-nets for
initial fusion and boundary fusion, respectively; these two sub-nets are
designed to first obtain a guidance map of FDB and then refine the fusion near
the FDB. Experiments demonstrate that with the help of the new {\alpha}-matte
boundary defocus model, the proposed MMF-Net outperforms the state-of-the-art
methods both qualitatively and quantitatively.
