An Algorithm of l1-Norm and l0-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection

被引:2
|
作者
Li, Xiezhang [1 ]
Feng, Guocan [2 ]
Zhu, Jiehua [1 ]
机构
[1] Georgia Southern Univ, Dept Math Sci, Statesboro, GA 30460 USA
[2] Sun Yat Sen Univ, Sch Math, Guangzhou 510275, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
NOISE;
D O I
10.1155/2020/8873865
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
The l(1)-norm regularization has attracted attention for image reconstruction in computed tomography. The l(0)-norm of the gradients of an image provides a measure of the sparsity of gradients of the image. In this paper, we present a new combined l(1)-norm and l(0)-norm regularization model for image reconstruction from limited projection data in computed tomography. We also propose an algorithm in the algebraic framework to solve the optimization effectively using the nonmonotone alternating direction algorithm with hard thresholding method. Numerical experiments indicate that this new algorithm makes much improvement by involving l(0)-norm regularization.
引用
收藏
页数:6
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