An Adaptive Proximal Point Algorithm for Sparse-View CT Image Reconstruction

被引：0
|
作者：
Zhu Y. [1 ]
Chen M.-Z. [1 ]
Chen Y. [2 ]
Yu G.-H. [2 ]
Wei L. [3 ]
机构：
[1] School of Physics and Electronics Information, Gannan Normal University, Ganzhou, 341000, Jiangxi
[2] School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, 341000, Jiangxi
[3] College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2019年 / 48卷 / 02期
关键词：
Computed tomography reconstruction; Proximal point; Saddle point problems; Sparse view; Total variation;
D O I：
10.3969/j.issn.1001-0548.2019.02.011
中图分类号：
学科分类号：
摘要：
This paper presents an adaptive proximal point algorithm (APPA) for sparse-view computed tomography (CT) image reconstruction based on total variation regularization. The proposed algorithm chooses an adaptive proximal parameter matrix which is neither necessary symmetric nor constant in each iteration. By using the framework of contraction method, the global convergence result could be established for the proposed algorithm under suitable conditions. Numerical results for 2-D CT reconstruction from simulated digital Shepp-Logan phantom data show that APPA method is effective and practical. © 2019, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.
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页码：228 / 232
页数：4
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