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

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 r...

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Vydáno v:Neural computing & applications Ročník 31; číslo 9; s. 5079 - 5096
Hlavní autoři: Lei, J., Liu, Q. B., Wang, X. Y.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.09.2019
Springer Nature B.V
Témata:
Artificial Intelligence
Capacitance
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Image Processing and Computer Vision
Image reconstruction
Imaging techniques
Optimization
Original Article
Probability and Statistics in Computer Science
Regularization
Splitting
Target recognition
Tomography
Three-operator splitting algorithm
Electrical capacitance tomography
Regularized random vector functional link network
Image reconstruction
Reference image regularization
Inverse problem
ISSN:0941-0643, 1433-3058
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Shrnutí: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.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-018-04000-z