Cart (Loading....) | Create Account
Close category search window
 

Image reconstruction based on L1 regularization and projection methods for electrical impedance tomography

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

7 Author(s)
Wang, Qi ; School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, People''s Republic of China ; Wang, Huaxiang ; Zhang, Ronghua ; Wang, Jinhai
more authors

Your organization might have access to this article on the publisher's site. To check, click on this link:http://dx.doi.org/+10.1063/1.4760253 

Electrical impedance tomography (EIT) is a technique for reconstructing the conductivity distribution by injecting currents at the boundary of a subject and measuring the resulting changes in voltage. Image reconstruction in EIT is a nonlinear and ill-posed inverse problem. The Tikhonov method with L2 regularization is always used to solve the EIT problem. However, the L2 method always smoothes the sharp changes or discontinue areas of the reconstruction. Image reconstruction using the L1 regularization allows addressing this difficulty. In this paper, a sum of absolute values is substituted for the sum of squares used in the L2 regularization to form the L1 regularization, the solution is obtained by the barrier method. However, the L1 method often involves repeatedly solving large-dimensional matrix equations, which are computationally expensive. In this paper, the projection method is combined with the L1 regularization method to reduce the computational cost. The L1 problem is mainly solved in the coarse subspace. This paper also discusses the strategies of choosing parameters. Both simulation and experimental results of the L1 regularization method were compared with the L2 regularization method, indicating that the L1 regularization method can improve the quality of image reconstruction and tolerate a relatively high level of noise in the measured voltages. Furthermore, the projected L1 method can also effectively reduce the computational time without affecting the quality of reconstructed images.

Published in:

Review of Scientific Instruments  (Volume:83 ,  Issue: 10 )

Date of Publication:

Oct 2012

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.