SAR and Infrared Image Fusion based on Latent Low-rank Representation

Cong Li, Meng Cai, P. Xu, Yi Liang
{"title":"SAR and Infrared Image Fusion based on Latent Low-rank Representation","authors":"Cong Li, Meng Cai, P. Xu, Yi Liang","doi":"10.23919/CISS51089.2021.9652254","DOIUrl":null,"url":null,"abstract":"To solve the problems of image information loss and spectral distortion during the fusion of SAR and infrared images, this paper proposes a SAR and infrared image fusion method based on Latent Low-Rank Representation (LatLRR). First, the method uses Non-Subsampled Contourlet Transform (NSCT) to obtain the low-frequency and high-frequency information of the source image. Then, the low-frequency information determines the fusion weight of the low-frequency part and the high-frequency uses LatLRR to extract low-rank components for adaptive weighted fusion. Finally, uses inverse NSCT transformation on the fusion coefficients to obtain the fusion image. Compared with other typical fusion methods, the proposed method has better visual effects, and the objective evaluation parameter values are also improved.","PeriodicalId":318218,"journal":{"name":"2021 2nd China International SAR Symposium (CISS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 2nd China International SAR Symposium (CISS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/CISS51089.2021.9652254","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

To solve the problems of image information loss and spectral distortion during the fusion of SAR and infrared images, this paper proposes a SAR and infrared image fusion method based on Latent Low-Rank Representation (LatLRR). First, the method uses Non-Subsampled Contourlet Transform (NSCT) to obtain the low-frequency and high-frequency information of the source image. Then, the low-frequency information determines the fusion weight of the low-frequency part and the high-frequency uses LatLRR to extract low-rank components for adaptive weighted fusion. Finally, uses inverse NSCT transformation on the fusion coefficients to obtain the fusion image. Compared with other typical fusion methods, the proposed method has better visual effects, and the objective evaluation parameter values are also improved.
基于潜在低秩表示的SAR与红外图像融合
针对SAR与红外图像融合过程中存在的图像信息丢失和光谱失真问题,提出了一种基于潜在低秩表示(Latent Low-Rank Representation, LatLRR)的SAR与红外图像融合方法。该方法首先利用非下采样Contourlet变换(NSCT)获取源图像的低频和高频信息;然后,低频信息确定低频部分的融合权重,高频部分使用LatLRR提取低秩分量进行自适应加权融合。最后,对融合系数进行NSCT逆变换,得到融合图像。与其他典型融合方法相比,该方法具有更好的视觉效果,并提高了客观评价参数值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信