An α-Matte Boundary Defocus Model-Based Cascaded Network for Multi-Focus Image Fusion

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 that are focused at different depths. However, existing multi-focus image fusion methods cannot obtain clear results for areas near the focused/defocused boundary (FDB). In this article, a novel α -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 α -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. Specifically, the MMF-Net consists of two cascaded subnets for initial fusion and boundary fusion. These two subnets 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 α -matte boundary defocus model, the proposed MMF-Net outperforms the state-of-the-art methods both qualitatively and quantitatively