Image Reconstruction Based on Convolutional Neural Network for Electrical Resistance Tomography

Image reconstruction is a key problem for electrical resistance tomography (ERT). Because of the soft-field nature and the ill-posed problem in solving inverse problem, traditional image reconstruction methods cannot achieve high accuracy and the process is usually time consuming. Since deep learning is good at mapping complicated nonlinear function, a deep learning method based on convolutional neural network (CNN) is proposed for image reconstruction of ERT. To establish the database, 41122 samples were generated with numerical simulations. 10-fold cross validation was used to divide all samples into training set and validation set. The network structure was based on LeNet, and refined by applying dropout layer and moving average. After 346 training epochs, the image correlation coefficient (ICC) on validation set was 0.95. When white Gaussian noise with a signal-to-noise ratio of 30, 40, and 50 were added to validation set, the ICC was 0.79, 0.89, and 0.93, respectively, which proved the anti-noise capability of the network. The reconstruction results on samples which have more inclusions, different conductivity, and other shapes explained the network has good generalization ability. Furthermore, experimental data from a 16-electrode industrial ERT system was used to compare the accuracy of the proposed model with some typical reconstruction methods. Results show that the proposed CNN method has better reconstruction results than LBP, Tikhonov, and Landweber.