无线电工程2025，Vol.55Issue(3)：463-474,12.DOI:10.3969/j.issn.1003-3106.2025.03.002
基于低秩稀疏分解的红外可见光图像融合技术研究
Research on Infrared-visible Fusion Technology Based on Low-rank Sparse Decomposition
摘要
Abstract
To solve the problem that the global structure and detail information cannot be preserved due to the different information features of the source image in current infrared and visible image fusion algorithms,a Low-rank Sparse Decomposition(LRSD)based infrared and visible image fusion method is proposed.In this method,the dictionary is constructed by Method of Optimal Directions(MOD),K-Singular Value Decomposition(K-SVD),and background dictionary,and then Low-rank Representation(LRR)is used to decompose the source image to obtain the low-rank part and the sparse detail part.The low-rank part preserves the global structure of the source image,and the sparse part highlights the local structure and detail information of the source image.In the fusion process,weighted average and l2-l1 norm constraint strategies are used to merge the low-rank and sparse parts respectively to preserve the global contrast and pixel intensity.The experimental results show that compared with classical fusion algorithms,the proposed method has significant improvements in image visual effects and quantitative evaluation indicators.The quantitative evaluation indexes of fusion images obtained with MOD and K-SVD dictionary training methods such as Mutual Information(MI),Structural Similarity Index(SSIM),Visual Information Fidelity(VIF),Standard Deviation(SD),and Peak Signal to Noise Ratio(PSNR)have been improved by approximately 6%,27%,9.6%,2.4%and 3.4%,respectively.Meanwhile,the fusion images obtained with background dictionary training method improve MI,SSIM,SD,Mean Squared Error(MSE),and PSNR by about 23%,29%,1.2%,33%and 4.5%,respectively.关键词
低秩稀疏分解/低秩表示/字典学习/图像融合Key words
LRSD/LRR/dictionary learning/image fusion分类
计算机与自动化引用本文复制引用
张思宇,江雪,侯晓赟..基于低秩稀疏分解的红外可见光图像融合技术研究[J].无线电工程,2025,55(3):463-474,12.基金项目
国家自然科学基金(62071255,61971241)National Natural Science Foundation of China(62071255,61971241) （62071255,61971241）
