基于深度学习的红外与可见光图像融合综述

图像融合技术是指从不同的源图像中提取并融合互补的信息，生成一幅信息量更丰富、对后续高级视觉任务提供足够支持的图像.红外与可见光图像融合（Infrared and Visible Image Fusion,IVIF）是图像融合领域的一个重要分支.近年来，深度学习技术在视觉计算领域表现出了良好的性能，尤其是基于自编码器、卷积神经网络、生成对抗网络等几种基于深度学习的IVIF技术得到了蓬勃发展.为此，对基于深度学习的IVIF算法的方法、数据集和评估指标等进行了总结和阐述；通过大量的实验，进行定性和定量的结果分析，对比了各类基于深度学习IVIF算法的性能；最后，讨论了该领域未来发展的一些前景和研究方向.

Deep learning-based infrared and visible image fusion: A survey

Image fusion technology refers to the extraction and fusion of complementary information from different source images to generate an image with more information and sufficient support for subsequent high-level visual tasks. Infrared and Visible Image Fusion (IVIF) is an important branch of image fusion. In recent years, deep learning technology has shown good performance in various fields of visual computing. In particular, several deep learning-based IVIF technologies, such as autoencoders, convolutional neural networks, and generative adversarial networks, have been developed vigorously. Therefore, this paper summarizes and expounds on the methods, data sets, and evaluation indicators of the IVIF algorithm based on deep learning. Through a large number of experiments, qualitative and quantitative results are analyzed, and the performance of various IVIF algorithms based on deep learning is compared. Finally, some prospects for the future development of this field are discussed.