We propose a multi-focus image fusion method, in which a fully convolutional network for focus detection (FD-FCN) is constructed. To obtain more precise focus detection maps, we propose to add skip layers in the network to make both detailed and abstract visual information available when using FD-FCN to generate maps. A new training dataset for the proposed network is constructed based on dataset CIFAR-10. The image fusion algorithm using FD-FCN contains three steps: focus maps are obtained using FD-FCN, decision map generation occurs by applying a morphological process on the focus maps, and image fusion occurs using a decision map. We carry out several sets of experiments, and both subjective and objective assessments demonstrate the superiority of the proposed fusion method to state-of-the-art algorithms.

我们提出了一种多焦点图像融合方法，其中构造了一个用于焦点检测的全卷积网络（FD-FCN）。为了获得更精确的焦点检测图，我们建议在网络中添加跳过层，以在使用FD-FCN生成图时提供详细的和抽象的视觉信息。基于数据集CIFAR-10构建了针对所建议网络的新训练数据集。使用FD-FCN的图像融合算法包含三个步骤：使用FD-FCN获得聚焦图，通过对聚焦图进行形态学处理来生成决策图，并使用决策图来进行图像融合。我们进行了几组实验，主观和客观评估都证明了所提出的融合方法优于最新算法的优越性。