Adaptive variational models for image decomposition  

作　　者：XU JianLou FENG XiangChu HAO Yan HAN Yu 

机构地区：[1]School of Science,Xidian University [2]School of Mathematics and Statistics,Henan University of Science and Technology

出　　处：《Science China(Information Sciences)》2014年第2期239-246,共8页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Nos.60872138,61105011,61271294,11101292)

摘　　要：Image decomposition refers to the splitting of an image into two or more components. In this paper, a clean image is separated into two parts： one is the cartoon component, consisting only of geometric structure, and the other is the oscillatory component, consisting of texture. Three parts for noisy image are considered： cartoon, texture, and noise. To better decompose an image, we propose two new variational models. In our models, two adaptive regularization terms are introduced. The two regularization terms are determined by an adaptive function which can discriminate the cartoon and texture of an image automatically. Experimental results illustrate the effectiveness of the proposed models for image decomposition.Image decomposition refers to the splitting of an image into two or more components. In this paper, a clean image is separated into two parts： one is the cartoon component, consisting only of geometric structure, and the other is the oscillatory component, consisting of texture. Three parts for noisy image are considered： cartoon, texture, and noise. To better decompose an image, we propose two new variational models. In our models, two adaptive regularization terms are introduced. The two regularization terms are determined by an adaptive function which can discriminate the cartoon and texture of an image automatically. Experimental results illustrate the effectiveness of the proposed models for image decomposition.

关 键 词：total variation image decomposition CARTOON TEXTURE 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]