Image separation using wavelet-complex shearlet dictionary  

作　　者：Shuaiqi Liu Shaohai Hu Yang Xiao 

机构地区：[1]Institute of Information Science, Beijing Jiaotong University [2]Beijing Key Laboratory of Advanced Information Science and Network Technology

出　　处：《Journal of Systems Engineering and Electronics》2014年第2期314-321,共8页系统工程与电子技术（英文版）

基　　金：supported by the Aviation Science Foundation(201120M5007);the Natural Science Foundation of Beijing(4102050)

摘　　要：This paper presents a new method for image separation through employing a combined dictionary consisting of wavelets and complex shearlets. Because the combined dictionary sparsely represents points and curvilinear singularities respectively, the image can be decomposed into pointlike and curvelike parts as accurate as possible. The proposed method based on the geo- metric separation theory introduced by Donoho in 2005 shows that accurate geometric separation of the morphologically distinct fea- tures of points and curves can be achieved by l1 minimization. The experimental results show that the proposed method can not only be effective but also greatly reduce the computing time.This paper presents a new method for image separation through employing a combined dictionary consisting of wavelets and complex shearlets. Because the combined dictionary sparsely represents points and curvilinear singularities respectively, the image can be decomposed into pointlike and curvelike parts as accurate as possible. The proposed method based on the geo- metric separation theory introduced by Donoho in 2005 shows that accurate geometric separation of the morphologically distinct fea- tures of points and curves can be achieved by l1 minimization. The experimental results show that the proposed method can not only be effective but also greatly reduce the computing time.

关 键 词：geometric separation l1 minimization sparse approx-imation complex shearlet. 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]