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 $\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. 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 $\alpha $ -matte boundary defocus model, the proposed MMF-Net outperforms the state-of-the-art methods both qualitatively and quantitatively.

中文翻译：

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基于α-哑光边界离焦模型的级联网络，用于多焦点图像融合。

用一台摄像机捕获全焦点图像非常困难，因为摄像机的景深通常受到限制。获得全焦点图像的另一种方法是融合几个聚焦在不同深度的图像。但是，现有的多焦点图像融合方法无法针对聚焦/散焦边界（FDB）附近的区域获得清晰的结果。在本文中，一本小说 $ \ alpha $ 提出了哑光边界散焦模型，以生成精确建模的散焦散布效果的实际训练数据，尤其是对于FDB附近的区域。基于此 $ \ alpha $ 为了消除散焦模型和生成的数据，提出并训练了一个称为MMF-Net的级联边界感知卷积网络，目的是在FDB周围实现更清晰的融合结果。具体来说，MMF-Net由两个用于初始融合和边界融合的级联子网组成。设计这两个子网是为了首先获取FDB的指导图，然后完善FDB附近的融合。实验表明，借助新的 $ \ alpha $ -哑光边界散焦模型，所提出的MMF-Net在质量和数量上均优于最新方法。