Ma Haoyu, Liao Qingmin, Zhang Juncheng, Liu Shaojun, Xue Jing-Hao
IEEE Trans Image Process. 2020 Aug 26;PP. doi: 10.1109/TIP.2020.3018261.
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 paper, 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.
用单台相机获取全焦图像很困难，因为相机的景深通常有限。获取全焦图像的另一种方法是融合多张聚焦于不同深度的图像。然而，现有的多聚焦图像融合方法在聚焦/散焦边界（FDB）附近区域无法获得清晰的结果。本文提出了一种新颖的α遮罩边界散焦模型，以生成逼真的训练数据，精确模拟散焦扩散效应，特别是对于FDB附近的区域。基于此α遮罩散焦模型和生成的数据，提出并训练了一种级联边界感知卷积网络，称为MMF-Net，旨在在FDB周围实现更清晰的融合结果。具体而言，MMF-Net由两个级联子网组成，分别用于初始融合和边界融合。这两个子网旨在首先获得FDB的引导图，然后在FDB附近细化融合。实验表明，借助新的α遮罩边界散焦模型，所提出的MMF-Net在定性和定量方面均优于现有方法。
