基于BP神经网络和中值滤波的ECT图像重构算法

电容层析成像技术（ECT-Electrical Capacitance Tomography）是基于电容敏感原理的过程成像技术,具有非侵入性、造价低、安装方便、实时性好等优点。图像重构作为ECT系统的关键技术,其实质是根据物体内部介电常数的空间分布推导出管道中各相分布的过程。本文针对重构问题的非线性、病态性等特点,采用了基于BP神经网络的ECT图像重构算法,并引入中值滤波对重构图像进行增强。仿真结果表明,该算法可以有效地实现图像重构和令人满意的增强效果,它大大提高了重建图像的质量,是一种有效的ECT图像重构算法。

An ECT image reconstruction algorithm using BP neural network and median filter

Electrical Capacitance Tomography（ECT） is a process imaging technology based on the principle of capacitance-sensitive with the advantages of non-invasive,low cost,easy installation and good real-time.Image reconstruction algorithm is an important factor of ECT system.Its essence is deriving the spatial distribution of each phase based on the dielectric constant of objects in the pipeline.For nonlinearity and morbid state of image reconstruction for electrical capacitance tomography system,this paper put forward BP neural network image reconstruction algorithm to fulfill the requirement of flow regime identification and emphatically analyzed median filter to realize image enhancement.Simulation results show that this algorithm can effectively achieve reconstruction and implement satisfactory image enhancement.It greatly improved the quality of reconstructed image and perspicuous image can be achieved.