Image reconstruction method of electrical capacitance tomography based on compressed sensing principle

Abstract

To improve the image reconstruction quality of electrical capacitance tomography (ECT), a new ECT image reconstruction method based on compressed sensing principle is proposed. The capacitances’ collection of the ECT system is regarded as linear measurement of compressed sensing; the measurement matrix is designed by randomly realigning the row vectors of a new sensitivity matrix, which is reconstructed by zero vectors’ expansion of the original sensitivity matrix, and the capacitance vectors expanded in the same way are regarded as measurement signals of compressed sensing. The original signal, i.e. permittivity distribution of some object inside a pipeline, is recovered by orthogonal matching pursuit algorithm. Finally, the simulation experiments are performed. The experimental results have shown that the average image reconstruction error and correlation coefficient obtained by this method are better than the corresponding indicators obtained by linear back-projection algorithm, Landweber iterative algorithm and total variation regularization algorithm. Therefore, it is a kind of ECT image reconstruction method with high accuracy, which also provides a new method and means for ECT research.

Electrical capacitance tomography image reconstruction by improved orthogonal matching pursuit algorithm

“…The main process of CS is the linear measurement, where a highdimensional signal x is projected to a low-dimensional measurement vector y using a measurement matrix Φ [22]. This process can be expressed as follows:…”

“…At present, reconstruction methods based on the CS theory have been extended for ECT image reconstruction [13,[19][20][21][22][23][24], and the grey-value vector of the image to be reconstructed is transformed into a sparse signal usually by a sparse transform [21][22][23][24]. However, the signal sparsity before and after the sparse transform is not compared, and the fidelity of the sparse signal obtained after the sparse transform to the original signal under different sparsity degrees is not considered.…”

A Limited Region Electrical Capacitance Tomography for Detection of Deposits in Pipelines

“…Numerous inversion algorithms were suggested for ECT image reconstruction [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]. However, due to the Ill-posedness, Ill-conditioning and Non-linearity of the ECT problem, the quality of the reconstructed images is still under research for enhancement.…”

Sparsity-inspired image reconstruction for electrical capacitance tomography

“…They showed that the method can produce fairly high quality reconstructions with clear edges and high precision. Another work by Wu et al [29] explored the EVT image reconstruction problem based on Compressive Sensing principles. The permittivity distribution of some object inside a pipeline was recovered by Orthogonal Matching Pursuit algorithm.…”