Compressed sensing framework for electrical resistance tomography

J. Zhao, C. Tan, F. Dong

Tianjin Key Laboratory of Process Measurement and Control

School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072,China

Abstract

Image reconstruction for electrical resistance tomography (ERT) is a nonlinear and ill-posed inverse problem. In order to improve the imaging quality, compressed sensing (CS) is applied to measured signal recovery and image reconstruction of ERT system in this paper. Firstly, CS is used to sample and recover the signal measured by ERT system. Secondly, a signal recovery algorithm of CS is used in the image reconstruction with the recovered signal. Simulation results show that CS can recover the voltage difference signal with a smaller sampling number, and the imaging results have less artifacts than other three imaging algorithms.

Keywords: Electrical resistance tomography; Compressed sensing; Signal recovery; Image reconstruction; Discrete cosine transform; L1 regularization; L2 regularization.