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ECT Image Reconstruction Method Based on Multi-Exponential Feature Extraction
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DOI：10.1109/TIM.2021.3132829.png)
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Abstract
En 中文
Electrical capacitance tomography (ECT) provides an effective way for solving the problem of two-phase flow measurement. However, there is a problem of underdetermination, which leads to the low accuracy of ECT image reconstruction. In this article, a new ECT image reconstruction method based on multi-exponential feature extraction is proposed to improve the accuracy of ECT image reconstruction. First, it can be observed that the grayscale of two-dimensional images is a typical Dirac pulse sequence, and the grayscale vector can be modeled as a discrete finite rate of innovation (FRI) signal. Then, the FRI sampling system is used to extract the feature information after filtering by exponential reproducing kernel. Finally, by means of zero filling and random recombination, a comprehensive observation equation can be constructed by using feature information. The original image signal is recovered by solving a L0 norm optimization problem. Simulation results have shown that the images reconstructed by the method in this article outperform those reconstructed by existing algorithms.
Keywords:
Image reconstruction
Electrodes
Sensitivity
Feature extraction
Capacitance
Tomography
Mathematical models
Electrical capacitance tomography (ECT)
finite rate of innovation (FRI)
image reconstruction
L0 norm
multi-exponential feature extraction
Journal
IF:
5.9
Papers:
2.0W
Citations:
5.8W
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Cited Papers
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