An Improved Total Variation Regularization Method for Electrical Resistance Tomography

被引：0

作者：

Song, Xizi ^{[1
]}

Xu, Yanbin ^{[1
]}

Dong, Feng ^{[1
]}

机构：

[1] Tianjin Univ, Sch Elect Engn & Automat, Tianjin Key Lab Proc Measurement & Control, Tianjin 300072, Peoples R China

来源：

PROCEEDINGS OF 2013 CHINESE INTELLIGENT AUTOMATION CONFERENCE: INTELLIGENT AUTOMATION & INTELLIGENT TECHNOLOGY AND SYSTEMS | 2013年 / 255卷

基金：

中国国家自然科学基金;

关键词：

Electrical resistance tomography (ERT); Image reconstruction; Tikhonov regularization; Total variation regularization; CAPACITANCE TOMOGRAPHY; ALGORITHMS;

D O I：

10.1007/978-3-642-38460-8_67

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Electrical resistance tomography (ERT) attempts to image the conductivity distribution of an object by measuring the voltage on its boundary. The reconstruction of ERT is an ill-posed inverse problem, and little noise in the measured data can cause large errors in the estimated conductivity. In this paper, an improved TV regularization method for ERT is introduced with iterations updated by the projected Gauss-Newton steps. It replaces the conventional TV regularization penalty term integral(Omega)vertical bar del g vertical bar d Omega by integral(Omega)vertical bar del g vertical bar(p) d Omega, in which p is selected as 1.5. The choice of such a penalty compromises both the conventional smoothness and discontinuities of the imaged conductivity. The improved approach can reconstruct images with sharp edges as well as reducing the suffering of the staircase effect. Simulation and experimental results of the improved method, TV regularization and Tikhonov regularization are compared, which show that this improved TV regularization can endure a relatively high level of noise in the measured voltages.

引用

页码：603 / 610

页数：8

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