An Autoencoder-Based Image Reconstruction for Electrical Capacitance Tomography

Electrical capacitance tomography (ECT) image reconstruction has developed decades and made great achievements, but there is still a need to find new theory framework to make image reconstruction results better and faster. Recent years, deep learning, which is based on different series of artificial neural networks good at mapping complicated nonlinear functions, is flourishing and adopted in many fields. In this paper, a supervised autoencoder neural network is proposed to solve the image reconstruction problem of ECT, which has an encoder network and a decoder network. A simulation-based data set consisting of 40 000 pairs of instances, of which each pair of sample has a capacitance vector and corresponding permittivity distribution vector, is used to train and test the performance of the autoencoder by 10-fold cross validation. Furthermore, data with artificial noise, data regarding flow pattern not in training data set, and experimental data from a practical ECT system, are used to test the generalization ability and practicability of the network, respectively. The preliminary results show that the proposed autoencoder-based image reconstruction algorithm for ECT is of providing better reconstruction results.