The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON CIRCUITS AND SYSTEMS
Transactions ID Number: 53-870
Full Name: Lingling Zhangling
Position: Please, select:
Age: ON
Sex: Female
Address: Tianjin University
Country: CHINA
Tel:
Tel prefix:
Fax:
E-mail address: zhanglingling@tju.edu.cn
Other E-mails:
Title of the Paper: A Fast Iterative Shrinkage-Thresholding Algorithm for
Authors as they appear in the Paper: Lingling Zhang, Huaxiang Wang, Yanbin Xu, Da Wang
Email addresses of all the authors: zhanglingling@tju.edu.cn,hxwang@tju.edu.cn, xuyanbin@tju.edu.cn, wangda@tju.edu.cn
Number of paper pages: 11
Abstract: Image reconstruction in Electrical Resistance Tomography (ERT) is an ill-posed nonlinear inverse problem. Considering the influence of the sparse measurement data on the quality of the reconstructed image, the l1 regularized least-squares program (l1 regularized LSP), which can be cast as a second order cone programming problem, is introduced to solve the inverse problem in this paper. A normally used method of implementing the l1 regularized LSP is based on the interior point method whose main drawback is the relatively slow convergence speed. To meet the need of high speed in ERT, the fast iterative shrinkage-thresholding algorithm (FISTA) is employed for image reconstruction in our work. Simulation results of the FISTA and l1_ls algorithm show that the l1 regularized LSP is superior to the l2 regularization method, especially in avoiding the over-smoothing of the reconstructed image. In addition, to improve the convergence speed and imaging quality in FISTA algo!
rithm, the initial guess is calculated with the conjugate gradient method. Comparative simulation results demonstrate the feasibility of FISTA in ERT system and its advantage over the l1_ls regularization method.
Keywords: Electrical resistance tomography; l1 Regularization method; Interior-point method; Iterative shrinkage-thresholding algorithm; Linear inverse problem
EXTENSION of the file: .pdf
Special (Invited) Session:
Organizer of the Session:
How Did you learn about congress:
IP ADDRESS: 202.113.4.204
