Image Reconstruction for High-Performance Electrical Capacitance Tomography System Using Deep Learning

Abstract

For great achievements in recent decades, image reconstruction for electrical capacitance tomography (ECT) has been considered in this study. ECT has demonstrated impressive potentials in multiprocess measurement, and the obtained images are of high resolution, which are suitable for advanced procedures in industrial and medical applications and across different tasks and domains. But the ECT system still requires improvements in the quality of image reconstruction given its importance of great significance to obtain the reliability and usefulness of measurement results. The deep neural network is used in this study to extract new features and to update the number of nodes and hidden layers in the system. Recently, deep learning exhibits suitable solutions in many flourishing fields based on different series of artificial neural networks for mapping nonlinear functions. To address the obstacles, this paper proposes an imaging method using an optimizer reconstruction model. An optimization model for imaging is generated as a powerful optimizer for building a computational model to ameliorate the reconstruction accuracy. Based on the deep learning methodology, the previous images reconstructed by using one of the imaging techniques to the required images are abstracted and stored in the deep learning machine, resulting in an error rate of 8.9%, and this is considered good on ECT. Therefore, an artificial neural network of the capacitance (ANNoC) system is introduced to estimate capacitance measurements.

Research on image reconstruction algorithm of capacitance imaging based on elastic network

“…With the development of intelligent algorithm, Li et al increased the training speed of ECT system image reconstruction algorithm and improved the imaging quality by simplifying the structure of neural network and reducing the size of neurons [4]. Zhang et al proposed a capacitance artificial neural network system to estimate the capacitance measurement value, reducing the error rate [5]. The intelligent algorithm can reliably identify simple flow patterns, but for some complex flow patterns, the intelligent algorithm still needs further improvement.…”