红外与可见光图像融合:统计分析,深度学习方法和未来展望
Infrared and Visible Image Fusion:Statistical Analysis,Deep Learning Approaches and Future Prospects
吴一非 1杨瑞 1吕其深 1唐雨婷 1张成敏 1刘帅辉1
作者信息
- 1. 江苏海洋大学电子工程学院,江苏 连云港 222005
- 折叠
摘要
图像融合旨在将来自不同源图像的互补信息融合在一起,生成一幅具有更高质量、更多信息量和更清晰的图像.红外与可见光图像融合(IVIF)是图像融合领域的研究热点,本文结合Systematic Review方法对近20年来三大工程类在线文献数据库相关的论文发表情况进行了统计分析和综述,重点分析和介绍了截至2023年8月基于深度学习的IVIF算法.此外,还系统地对IVIF领域的性能评价方法进行了详细分析,分类比较各种评价方法公式和具体元素的含义.最后,对IVIF未来技术趋势进行了总结和展望,可为IVIF未来的新技术研究提供参考.
Abstract
Image fusion aims to integrate complementary information from diverse source images,generating a composite image with higher quality,increased information content,and enhanced clarity.Infrared and visible light image fusion(IVIF)stands out as a focal point in the field of image fusion.This paper employs the Systematic Review method to conduct a comprehensive analysis and review of the publication trends in the last two decades within three major engineering online literature databases related to IVIF.The focus is on an in-depth examination and presentation of IVIF algorithms based on deep learning till August 2023.Additionally,a systematic analysis of performance evaluation methods in the IVIF domain is provided,including a categorized comparison of various evaluation method formulas and their specific components.Finally,the paper concludes with a summary and outlook on the future technological trends in IVIF,offering valuable insights for prospective research in this field.
关键词
图像融合/红外图像/可见光图像/深度学习/传统方法Key words
image fusion/infrared image/visible image/deep learning/traditional method引用本文复制引用
基金项目
江苏省"六大人才高峰"项目(XYDXXJS-009)
出版年
2024
NETL
NSTL
万方数据