光学精密工程2024，Vol.32Issue(10)：1567-1581,15.DOI:10.37188/OPE.20243210.1567
基于二次图像分解的红外图像与可见光图像融合
Infrared image and visible image fusion algorithm based on secondary image decomposition
摘要
Abstract
In view of the serious detail loss,the feature information of infrared image is not highlighted and the semantic information of source image is ignored in the fusion of infrared image and visible image,a fu-sion network of infrared image and visible image based on secondary image decomposition was proposed.The encoder was used to decompose the source image twice to extract the feature information of different scales,then the two-element attention was used to assign weights to the feature information of different scales,the global semantic branch is introduced,the pixel addition method was used as the fusion strate-gy,and the fusion image was reconstructed by the decoder.In the experiment,FLIR data set was select-ed for training,TNO and RoadScene data sets were used for testing,and eight objective evaluation param-eters of image fusion were selected for comparative analysis.The image fusion experiment of TNO data set shows that in terms of information entropy,standard deviation,spatial frequency,visual fidelity,aver-age gradient and difference correlation coefficient,SIDFuse is 12.2%,9.0%,90.2%,13.9%,85.1%,16.8%,6.7%,30.7%higher than DenseFuse,the classical fusion algorithm based on convolu-tional networks,respectively.Compared with the latest fusion network LRRNet,the average increase is 2.5%,5.6%,31.5%,5.4%,25.2%,17.9%,7.5%,20.7 respectively.It can be seen that the image fusion algorithm proposed in this paper has a high contrast,and can retain the detail texture of visible im-age and the feature information of infrared image more effectively at the same time,which has obvious ad-vantages in similar methods.关键词
图像融合/图像二次分解/全局语义支路/双元素注意力/图像对比度Key words
image fusion/image secondary decomposition/global semantic branch/two-element atten-tion/image contrast分类
信息技术与安全科学引用本文复制引用
马鑫,喻春雨,童亦新,张俊..基于二次图像分解的红外图像与可见光图像融合[J].光学精密工程,2024,32(10):1567-1581,15.基金项目
国家自然科学基金资助项目(No.61801239) （No.61801239）
中央高校基本科研业务费专项资金资助项目(No.30918014106) （No.30918014106）
南京邮电大学校企合作项目(No.2018外002,No.2019外157) （No.2018外002,No.2019外157）
