Abstract: How to preserve the complementary information in multiple images to represent the scene in one image is a challenging topic. Based on this topic, various image fusion methods have been proposed. As an important branch of image fusion, infrared and visible image fusion (IVIF) has a wide range of applications in segmentation, target detection and military reconnaissance fields. In recent years, deep learning has led the development direction of image fusion. Researchers have explored the field of IVIF using deep learning. Relevant experimental work has proven that applying deep learning to achieving IVIF has significant advantages compared with traditional methods. This paper provides a detailed analysis on the advanced algorithms for IVIF based on deep learning. Firstly, this paper reports on the current research status from the aspects of network architecture, method innovation, and limitations. Secondly, this paper introduces the commonly used datasets in IVIF methods and provides the definition of commonly used evaluation metrics in quantitative experiments. Qualitative and quantitative evaluation experiments of fusion and segmentation and fusion efficiency analysis experiments are conducted on some representative methods mentioned in the paper to comprehensively evaluate the performance of the methods. Finally, this paper provides conclusions and prospects for possible future research directions in the field.

Key words: image fusion, infrared and visible images, deep learning

摘要： 如何将多张图像中的互补信息保存到一张图像中用于全面表征场景是具有挑战性的课题。基于此课题，大量的图像融合方法被提出。红外可见光图像融合（IVIF）作为图像融合中一个重要分支，在语义分割、目标检测和军事侦察等实际领域都有着广泛的应用。近年来，深度学习技术引领了图像融合的发展方向，研究人员利用深度学习针对IVIF方向进行了探索。相关实验工作证明了应用深度学习方法来完成IVIF相较于传统方法有着显著优势。对基于深度学习的IVIF前沿算法进行了详细的分析论述。首先，从网络架构、方法创新以及局限性等方面报告了领域内的方法研究现状。其次，对IVIF方法中常用的数据集进行了简要介绍并给出了定量实验中常用评价指标的定义。对提到的一些具有代表性的方法进行了图像融合和语义分割的定性评估、定量评估实验以及融合效率分析实验来全方面地评估方法的性能。最后，给出了实验结论并对领域内未来可能的研究方向进行了展望。

关键词: 图像融合, 红外可见光图像, 深度学习