红外与可见光图像深度学习融合方法综述

A review of deep learning fusion methods for infrared and visible images

红外与可见光图像融合技术充分利用不同传感器的优势,在融合图像中保留了原图像的互补信息以及冗余信息,提高了图像质量。近些年,随着深度学习方法的发展,许多研究者开始将该方法引入图像融合领域,并取得了丰硕的成果。根据不同的融合框架对基于深度学习的红外与可见光图像融合方法进行归类、分析、总结,并综述常用的评价指标以及数据集。另外,选择了一些不同类别且具有代表性的算法模型对不同场景图像进行融合,利用评价指标对比分析各算法的优缺点。最后,对基于深度学习的红外与可见光图像融合技术研究方向进行展望,总结红外与可见光融合技术,为未来研究工作奠定基础。

Infrared and visible image fusion technology makes full use of the advantages of different sensors,retains the complementary information and redundant information of the original image in the fused image,and improves the image quality.In recent years,with the development of deep learning methods,many researchers have begun to introduce this method into the field of image fusion,and have achieved fruitful results.According to different fusion frameworks,the infrared and visible image fusion methods based on deep learning are classified,analyzed and summarized,the commonly used evaluation indicators and data sets are reviewed.In addition,some representative algorithm models of different categories are selected to fuse different scene images,the advantages and disadvantages of each algorithm are compared and analyzed by evaluation indicators.Finally,the research direction of infrared and visible image fusion technology based on deep learning is prospected,infrared and visible fusion technology is summarized,which is the basis for future research work.