ECT Imaging System Based on Lorentz Deblurring and Particle Filtering

Abstract

In oil and gas related industries, multiphase flow in pipelines is one of the important elements that the Industrial Pipeline Internet of Things (IoT) should continuously monitor. However, existing reconstruction methods often are limited by low resolution and blurred edges. In this article, an electrical capacitance tomography (ECT) imaging system based on Lorentz deblurring and particle filtering is proposed to suppress ECT image blurring. First, a deblurring model based on Lorentz function fitting is proposed, capable of effectively improving the blurred edges, through point spread function (PSF) estimation and Lucy-Richardson algorithm. Then, in the image reconstruction process, it is reformulated as an iterative search for effective particles and their associated weights in the state space, combined with Lorentz deblurring model for the optimal solution. Finally, the virtual-instrument-based ECT hardware system based on the principle of modularity, creates the synergistic architecture between the ECT hardware and imaging software, which enables real-time visualization of imaging. Simulation experiments demonstrate that the image reconstruction algorithm outperforms existing methods in terms of relative error and correlation coefficient, effectively suppressing image blur. Moreover, the ECT imaging system proposed can enhance the measurement capacitance accuracy.