An adaptive non-convex hybrid total variation regularization method for image reconstruction in electrical impedance tomography

The image reconstruction of conductivity distribution in electrical impedance tomography (EIT) is a seriously ill-posed inverse problem. To cope with the problem, it is recognized that the regularization method is an effective approach. In this paper, an adaptive non-convex hybrid total variation (A...

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Veröffentlicht in:Flow measurement and instrumentation Jg. 79; S. 101937
Hauptverfasser: Shi, Yanyan, Zhang, Xu, Wang, Meng, Fu, Feng, Tian, Zhiwei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.06.2021
Schlagworte:
Conductivity distribution
Electrical impedance tomography (EIT)
Image reconstruction
Conductivity distribution
Electrical impedance tomography (EIT)
Image reconstruction
ISSN:0955-5986, 1873-6998
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Zusammenfassung:The image reconstruction of conductivity distribution in electrical impedance tomography (EIT) is a seriously ill-posed inverse problem. To cope with the problem, it is recognized that the regularization method is an effective approach. In this paper, an adaptive non-convex hybrid total variation (ANHTV) regularization method is proposed to reconstruct the conductivity distribution in EIT. The iterative reweighted least squares algorithm and the iterative alternating direction method of multipliers algorithm are developed to solve the ANHTV-based inverse model in the image reconstruction. Besides, all the parameters utilized in the inverse model are adaptively selected. To validate the advantage of the proposed method, extensive numerical simulation and experimental work have been carried out. Also, qualitative and quantitative comparisons with two convex TV-based regularization methods are conducted. The results show that the proposed method is more advantageous in terms of staircase effect suppression, edge information preservation and noise resisting in the image reconstruction. •A non-convex hybrid total variation regularization method is proposed for image reconstruction in EIT.•All the essential parameters in the proposed method are adaptively selected.•The staircase effect is suppressed and the edge information is preserved.
ISSN:0955-5986
1873-6998
DOI:10.1016/j.flowmeasinst.2021.101937