Linearized Proximal Alternating Direction Method of Multipliers for Parallel Magnetic Resonance Imaging

Benxin Zhang; Zhibin Zhu

doi:10.1109/JAS.2016.7510226

Volume 4 Issue 4

Oct. 2017

IEEE/CAA Journal of Automatica Sinica

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Benxin Zhang and Zhibin Zhu, "Linearized Proximal Alternating Direction Method of Multipliers for Parallel Magnetic Resonance Imaging," IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 763-769, Oct. 2017. doi: 10.1109/JAS.2016.7510226

Citation:

Benxin Zhang and Zhibin Zhu, "Linearized Proximal Alternating Direction Method of Multipliers for Parallel Magnetic Resonance Imaging," IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 763-769, Oct. 2017. doi: 10.1109/JAS.2016.7510226

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Linearized Proximal Alternating Direction Method of Multipliers for Parallel Magnetic Resonance Imaging

doi: 10.1109/JAS.2016.7510226

1.
School of Electronic Engineering and Automation and Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic Technology, Guilin 541004, China
2.
School of Mathematics and Computing Science and Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin 541004

Funds:

the National Natural Science Foundation of China 11361018

the National Natural Science Foundation of China 11461015

Guangxi Natural Science Foundation 2014GXNSFFA118001

Guangxi Key Laboratory of Automatic Detecting Technology and Instruments YQ15112

Guangxi Key Laboratory of Automatic Detecting Technology and Instruments YQ16112

Guilin Science and Technology Project 20140127-2

the Innovation Project of Guangxi Graduate Education and Innovation Project of GUET Graduate Education YJCXB201502

Guangxi Key Laboratory of Cryptography and Information Security GCIS201624

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Abstract

Abstract

In this study, we propose a linearized proximal alternating direction method with variable stepsize for solving total variation image reconstruction problems. Our method uses a linearized technique and the proximal function such that the closed form solutions of the subproblem can be easily derived. In the subproblem, we apply a variable stepsize, that is like Barzilai-Borwein stepsize, to accelerate the algorithm. Numerical results with parallel magnetic resonance imaging demonstrate the efficiency of the proposed algorithm.
- Alternating direction method,
- Barzilai-Borwein stepsize,
- parallel magnetic resonance imaging,
- total variation image reconstruction

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References

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Linearized Proximal Alternating Direction Method of Multipliers for Parallel Magnetic Resonance Imaging

doi: 10.1109/JAS.2016.7510226

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