Abstract This paper proposes a two-stage convolutional neural network (CNN) fusion method that can obtain accurate decision map and deal with the problem of unclear fusion boundaries. In the first stage, an improved densenet is trained to classify whether the image patch is in focus or defocus, and then the corresponding fusion rule is utilized to acquire a perfect decision map. In addition, a multi-version blurred dataset is designed to improve the generalization ability of the network. In the second stage, edge-deblurring generative adversarial networks (EDGAN) is introduced to process the boundary. Furthermore, five different loss functions are applied to generate approving boundary deblurred images. At the same time, natural images are selected from the COCO dataset for special processing to simulate the boundary blurring situation to create the second-stage dataset. After two stages of processing, an image with rich details and decent fusion boundaries are attained. Experimental results demonstrate that the proposed algorithm is superior to other fusion algorithms in subjective vision and objective assessment.

中文翻译：

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基于两阶段卷积神经网络的多焦点图像融合方法

摘要 本文提出了一种两阶段卷积神经网络（CNN）融合方法，可以获得准确的决策图，处理融合边界不清楚的问题。在第一阶段，训练改进的密集网络以对图像块是聚焦还是散焦进行分类，然后利用相应的融合规则获得完美的决策图。此外，设计了多版本模糊数据集以提高网络的泛化能力。在第二阶段，引入边缘去模糊生成对抗网络（EDGAN）来处理边界。此外，应用五种不同的损失函数来生成认可的边界去模糊图像。同时，从 COCO 数据集中选取自然图像进行特殊处理，模拟边界模糊情况，创建第二阶段数据集。经过两个阶段的处理，获得了具有丰富细节和良好融合边界的图像。实验结果表明，该算法在主观视觉和客观评价方面均优于其他融合算法。