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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 9/2019

11.01.2019 | Original Article

Three-operator splitting scheme with the reference image regularization for electrical capacitance tomography

verfasst von: J. Lei, Q. B. Liu, X. Y. Wang

Erschienen in: Neural Computing and Applications | Ausgabe 9/2019

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Abstract

The image reconstruction is an important step in the electrical capacitance tomography (ECT) technology, and its performance directly impacts the reconstruction precision (RP). Beyond existing optimization-based imaging techniques, in this study the data-dependent reference image abstracted by the regularized random vector functional link network (RVFLN) and the domain expertise about imaging targets (ITs) are simultaneously encapsulated as regularizers to form a more effective imaging model. The three-operator splitting (TOS) technique is developed to solve the proposed imaging model more effectively, which extends the flexibility of the TOS method with the improvement in the utilization of image priors. Numerical validation results indicate that the proposed imaging technique achieves better reconstructions as compared with the state-of-the-art methods.
Vorheriger Artikel Convergence in Orlicz spaces by means of the multivariate max-product neural network operators of the Kantorovich type and applications
Nächster Artikel A fast bilateral filtering algorithm based on rising cosine function

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Metadaten
Titel
Three-operator splitting scheme with the reference image regularization for electrical capacitance tomography
verfasst von
J. Lei
Q. B. Liu
X. Y. Wang
Publikationsdatum
11.01.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 9/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-04000-z

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