Total fractional-order variation regularization based image reconstruction method for capacitively coupled electrical resistance tomography

Y Shi, J Liao, M Wang, M Li, F Feng, Manuchehr Soleimani

Research output: Contribution to journalArticlepeer-review

1 Citation (SciVal)
1 Downloads (Pure)

Abstract

Compared with electrical resistance tomography, capacitively coupled electrical resistance tomography (CCERT) is preferred since it avoids problems of electrode corrosion and electrode polarization. However, reconstruction of conductivity distribution is still a great challenge for CCERT. To improve reconstruction quality, this work proposes a novel image reconstruction method based on total fractional-order variation regularization. Simulation work is conducted and reconstruction of several typical models is studied. Robustness of the proposed method to noise is also conducted. Additionally, the performance of the proposed reconstruction method is quantitatively evaluated. We have also carried out phantom experiment to further verify the effectiveness of the proposed method. The results demonstrate that the quality of reconstruction has been largely improved when compared with the images reconstructed by Landweber, Newton-Raphson and Tikhonov methods. The inclusion is more accurately reconstructed and the background is much clearer even under the impact of noise.
Original languageEnglish
Article number102081
JournalFlow Measurement and Instrumentation
Volume82
Early online date15 Nov 2021
DOIs
Publication statusPublished - 31 Dec 2021

Fingerprint

Dive into the research topics of 'Total fractional-order variation regularization based image reconstruction method for capacitively coupled electrical resistance tomography'. Together they form a unique fingerprint.

Cite this