A primal dual proximal point method of Chambolle-Pock algorithms for ℓ1-TV minimization problems in image reconstruction

Computed tomography (CT) image reconstruction problems can be solved by finding the minimizer of a suitable objective function. The objective function usually consists of a data fidelity term and a regularization term. Total variation (TV) minimization problems are widely used for solving incomplete...

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Bibliographic Details
Published in2012 5th International Conference on Biomedical Engineering and Informatics pp. 12 - 16
Main Authors Tang, Yuchao, Peng, Jigen, Yue, Shigang, Xu, Jiawei
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2012
Subjects
Computed tomography
Convex functions
Data models
Image reconstruction
Linear programming
Noise
Signal to noise ratio
Total variance
Vectors
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Summary:Computed tomography (CT) image reconstruction problems can be solved by finding the minimizer of a suitable objective function. The objective function usually consists of a data fidelity term and a regularization term. Total variation (TV) minimization problems are widely used for solving incomplete data problems in CT image reconstruction. In this paper, we focus on the CT image reconstruction model which combines the TV regularization and ℓ 1 data error term. We introduce a primal dual proximal point method of Chambolle-Pock algorithm to solve the proposed optimization problem. We tested it on computer simulated data and the experiment results shown it exhibited good performance when used to few-view CT image reconstruction.
ISBN:9781467311830
1467311839
DOI:10.1109/BMEI.2012.6513092