Image fusion, which refers to extracting and then combining the most meaningful information from different source images, aims to generate a single image that is more informative and beneficial for subsequent applications. The development of deep learning has promoted tremendous progress in image fusion, and the powerful feature extraction and reconstruction capabilities of neural networks make the fused results promising. Recently, several latest deep learning technologies have made image fusion explode, e.g., generative adversarial networks, autoencoder, etc. However, a comprehensive review and analysis of latest deep-learning methods in different fusion scenarios is lacking. To this end and in this survey, we first introduce the concept of image fusion, and classify the methods from the perspectives of the deep architectures adopted and fusion scenarios. Then, we review the state-of-the-art on the use of deep learning in various types of image fusion scenarios, including the digital photography image fusion, the multi-modal image fusion and the sharpening fusion. Subsequently, the evaluation for some representative methods in specific fusion tasks are performed qualitatively and quantitatively. Moreover, we briefly introduce several typical applications of image fusion, including photography visualization, RGBT object tracking, medical diagnosis, and remote sensing monitoring. Finally, we provide the conclusion, highlight the challenges in image fusion, and look forward to potential future research directions.

图像融合是指从不同的源图像中提取并组合最有意义的信息，旨在生成一个信息量更大、有利于后续应用的单一图像。深度学习的发展推动了图像融合的巨大进步，神经网络强大的特征提取和重建能力使融合结果大有可为。最近，一些最新的深度学习技术使图像融合爆炸式增长，例如生成对抗网络、自动编码器等. 然而，缺乏对不同融合场景下最新深度学习方法的全面回顾和分析。为此，在本次调查中，我们首先介绍了图像融合的概念，并从所采用的深层架构和融合场景的角度对方法进行了分类。然后，我们回顾了在各种类型的图像融合场景中使用深度学习的最新技术，包括数字摄影图像融合、多模态图像融合和锐化融合。随后，对特定融合任务中的一些代表性方法进行定性和定量评估。此外，我们简要介绍了图像融合的几个典型应用，包括摄影可视化、RGBT 对象跟踪、医学诊断和遥感监测。最后，