An {\alpha}-Matte Boundary Defocus Model Based Cascaded Network for Multi-focus Image Fusion

TL;DR

This paper introduces a novel {}-matte boundary defocus model and a cascaded neural network, MMF-Net, to improve multi-focus image fusion, especially near focused/defocused boundaries, achieving superior results.

Contribution

The paper proposes a new {}-matte boundary defocus model for realistic training data and a cascaded boundary-aware network, MMF-Net, for enhanced multi-focus image fusion near boundaries.

Findings

MMF-Net outperforms existing methods qualitatively and quantitatively.

The {}-matte defocus model effectively simulates defocus spread effects.

Boundary-aware fusion improves clarity near focused/defocused boundaries.

Abstract

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.