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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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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Abstract 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.
AbstractList 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.
Author Tang, Yuchao
Peng, Jigen
Yue, Shigang
Xu, Jiawei
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  organization: School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
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Snippet Computed tomography (CT) image reconstruction problems can be solved by finding the minimizer of a suitable objective function. The objective function usually...
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StartPage 12
SubjectTerms Computed tomography
Convex functions
Data models
Image reconstruction
Linear programming
Noise
Signal to noise ratio
Total variance
Vectors
Title A primal dual proximal point method of Chambolle-Pock algorithms for ℓ1-TV minimization problems in image reconstruction
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