Image Reconstruction in Electrical Capacitance Tomography Based on Deep Neural Networks

Electrical Capacitance Tomography (ECT) image reconstruction has been largely applied for industrial applications. However, there is still a crucial need to develop a new framework to enhance the quality of reconstructed images and make it faster. Deep learning has recently boomed and applied in many fields since it is good at mapping complicated nonlinear functions based on series of artificial neural networks. In this paper, a novel image reconstruction method based on a deep neural network is proposed. The proposed image reconstruction algorithm mainly uses Long Short-Term Memory (LSTM) deep neural network, which is abbreviated as LSTM-IR algorithm. A big simulation dataset containing 160k pairs of instances is created to train and test the performance of the proposed LSTM-IR algorithm. Each pair of the sample has a predefined permittivity distribution vector and corresponding capacitance vector. The generalization ability and feasibility of the LSTM-IR network are measured using contaminated data, data not included in the training dataset, and experimental data. The preliminary results show that the proposed LSTM-IR method can create fast and more accurate ECT images than traditional and deep learning image reconstruction algorithms.

Automated summary

This paper proposes a novel image reconstruction method based on a deep neural network utilizing Long Short-Term Memory (LSTM) network. Through training and testing on a large simulation dataset, it is found that the proposed LSTM-IR algorithm can create ECT images faster and more accurately compared to traditional and deep learning image reconstruction algorithms.