In this study, we propose a new deep learning architecture named Multi-Level Dense Network (MLDNet) for multi-focus image fusion (MFIF). We introduce shallow and dense feature extraction in our feature extraction module to extract images features in a more robust way. In particular, we extracted the features from a mixture of many distributions from prior to the complex distribution through densely connected convolutional layers, then the extracted features are fused to form dense local feature maps. We added global feature fusion into the proposed architecture in order to merge the dense local feature maps of each source image into a fused image representation for the reconstruction of the final fused image. Our proposed MLDNet learns feature extraction, feature fusion and reconstruction within the same network to provide an end-to-end solution for MFIF. Experimental results demonstrate that our proposed method achieved significant performance against different state-of-the-art MFIF methods.

在这项研究中，我们提出了一种新的深度学习架构，称为多级密集网络（MLDNet），用于多焦点图像融合（MFIF）。我们在特征提取模块中引入了浅层和密集的特征提取，以更可靠的方式提取图像特征。特别是，我们通过密集连接的卷积层从复杂分布之前的许多分布的混合中提取特征，然后融合提取的特征以形成密集的局部特征图。为了将每个源图像的密集局部特征图合并到融合图像表示中，以重建最终融合图像，我们在提议的体系结构中添加了全局特征融合。我们建议的MLDNet学习特征提取，在同一网络中进行特征融合和重建，以提供MFIF的端到端解决方案。实验结果表明，我们提出的方法相对于不同的最新MFIF方法具有显着的性能。