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Image Filtering With Associative Markov Networks for ECT With Distinctive Phase Origins

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1 Author(s)
Jiamin Ye ; Institute of Particle Science and Engineering, School of Process, Environmental, and Materials Engineering, University of Leeds, Leeds, U.K.

The images reconstructed by electrical capacitance tomography (ECT) for two-phase flows are usually blurry at the phase interface. To improve the image quality, image filtering with associative Markov networks (AMNs), which support efficient graph-cut inference for insulation segmentation, is presented. An ECT sensor with 12 electrodes is investigated and the capacitance between different electrode pairs is calculated for some typical permittivity distributions using a finite element method. The initial images are reconstructed by liner back-projection and Landweber iterative algorithm, respectively. The obtained images are then processed using AMNs. Simulation results show significant improvement in the quality of images.

Published in:

IEEE Sensors Journal  (Volume:12 ,  Issue: 7 )