红外技术2026，Vol.48Issue(8)：903-913,11.
基于深度学习的红外与可见光图像融合研究进展
Review of Deep Learning-Based Infrared and Visible Image Fusion
摘要
Abstract
This paper presents the results of a systematic study on infrared and visible image fusion(IVIF)technology based on deep learning algorithms.First,it comprehensively elaborates on the research status of mainstream deep learning models used for IVIF tasks;discusses the results of an in-depth analysis of the framework structures,core characteristics,and computational complexities of various methods;and reveals the advantages and disadvantages of different models for information extraction and reconstruction.Second,it systematically sorts the subjective and objective evaluation systems for IVIF and analyzes the focus dimensions of the subjective evaluation and applicable scenarios of objective evaluation metrics in detail.Third,the currently publicly available and commonly used IVIF datasets are summarized to provide a reference for algorithm verification and comparison.Finally,aiming at the shortcomings of the existing methods,future development trends for IVIF in the directions of model lightweighting,registration adaptability,and task-driven fusion are discussed.关键词
红外图像/可见光图像/图像融合/深度学习Key words
infrared image/visible image/image fusion/deep learning分类
信息技术与安全科学引用本文复制引用
李云红,何舒扬,邢校菖,朱耀麟,廖文馨,王宁,陶闯..基于深度学习的红外与可见光图像融合研究进展[J].红外技术,2026,48(8):903-913,11.基金项目
国家自然科学基金青年基金(52505435) （52505435）
陕西省自然科学基础研究计划重点项目(2022JZ-35) （2022JZ-35）
西安市"科学家+工程师"队伍建设项目(25KGYB00029). （25KGYB00029）
