Q. Xie, J. He, L. Qian, S. Mita, X. Chen, A. Jiang
{"title":"Image fusion based on TV-L1 function","authors":"Q. Xie, J. He, L. Qian, S. Mita, X. Chen, A. Jiang","doi":"10.1109/ICWAPR.2013.6599312","DOIUrl":null,"url":null,"abstract":"This paper solves the image fusion problem by TV-L1 energy function. The energy function mainly consists of two components. One ensures the injection of correlated detail spatial information by using the total variation (TV) method. The other integrates the detail information from gradient representation into the fused result based on the TV method. The spectral information is preserved through L1 norm based on data fitting term. The main feature of the fusion formulation is that it obtains more accurate spectral information through L1 norm and directly injects the fused result with the spatial gradient information with TV term. Since the energy function is non-smooth, the corresponding fused band with the minimum energy is obtained through primal-dual hybrid gradient algorithm. Experimental results demonstrate the superiority of the proposed method over some classical methods.","PeriodicalId":236156,"journal":{"name":"2013 International Conference on Wavelet Analysis and Pattern Recognition","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Wavelet Analysis and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICWAPR.2013.6599312","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
This paper solves the image fusion problem by TV-L1 energy function. The energy function mainly consists of two components. One ensures the injection of correlated detail spatial information by using the total variation (TV) method. The other integrates the detail information from gradient representation into the fused result based on the TV method. The spectral information is preserved through L1 norm based on data fitting term. The main feature of the fusion formulation is that it obtains more accurate spectral information through L1 norm and directly injects the fused result with the spatial gradient information with TV term. Since the energy function is non-smooth, the corresponding fused band with the minimum energy is obtained through primal-dual hybrid gradient algorithm. Experimental results demonstrate the superiority of the proposed method over some classical methods.