Abstract: Multi-focus image fusion is an effective image fusion technology, which aims to combine source images from different focal planes of the same scene to obtain a good fusion result. This means that the fused image will focus on all focal planes, that is, it contains more abundant scene information. The development of deep learning promotes the great progress of image fusion, and the powerful feature extraction and reconstruction ability of neural network makes the fusion result promising. In recent years, more and more multi-focus image fusion methods based on deep learning have been proposed, such as convolutional neural network (CNN), generative adversarial network (GAN) and automatic encoder, etc. In order to provide effective reference for relevant researchers and technicians, firstly, this paper introduces the concept of multi-focus image fusion and some evaluation indicators. Then, it analyzes more than ten advanced methods of multi-focus image fusion based on deep learning in recent years, discusses the characteristics and innovation of various methods, and summarizes their advantages and disadvantages. In addition, it reviews the application of multi-focus image fusion technology in various scenes, including photographic visualization, medical diagnosis, remote sensing detection and other fields. Finally, it proposes some challenges faced by current multi-focus image fusion related fields and looks forward to future possible research trends.

Key words: deep learning, image fusion, multi-focus

摘要： 多聚焦图像融合是一种有效的图像融合技术，旨在将来自同一场景的不同焦平面的源图像组合起来以获得良好的融合结果，这意味着融合后的图像将在所有焦平面都聚焦，也就是包含着更丰富的场景信息。深度学习的发展促进了图像融合的巨大进步，而神经网络强大的特征提取和重构能力使融合结果大有可为。近年来，出现了越来越多的基于深度学习的多聚焦图像融合方法，如卷积神经网络（CNN）、生成对抗网络（GAN）和自动编码器等。为了给相关研究人员和技术人员提供有效的参考和理解，介绍了多聚焦图像融合的概念和一些评价指标，分析了十几种近几年基于深度学习的多聚焦图像融合前沿方法，讨论了各种方法的特点和创新性，并总结了它们的优缺点，回顾了多聚焦图像融合技术在各种场景中的应用，包括摄影可视化、医疗诊断和遥感检测等领域，介绍了目前多聚焦图像融合相关领域面临的一些挑战并展望了未来可能的研究趋势。

关键词: 深度学习, 图像融合, 多聚焦