Big Data driven U-Net based Electrical Capacitance Image Reconstruction Algorithm

Abstract

An efficiency electrical capacitance image reconstruction method which combines fully connected neural network and U-Net structure, is put forward for the first time in electrical capacitance tomography (ECT) area in this paper. Since the target of ECT image reconstruction can also be considered as an image segmentation problem-which U-Net structure is designed for. In this paper, the Convolutional Neural Network (CNN) based U-Net structure is used to improve the quality of images reconstructed by ECT. Firstly, about 60,000 data samples with different patterns are generated by the cosimulation of COMSOL Multiphysic and MATLAB. Then a fully connected neural network (FC) is used to pre-process these samples to get initial reconstructions which are not accurate enough. Finally, U-Net structure is used to further process those pre-trained images and will output reconstructed images with both high speed and quality. The robustness, generalization and practicability ability of the U-Net structure is proved. As stated in Section2, it illustrates that U-Net structure matches properly with ECT image reconstruction problems due to its antoencoder strcture. The preliminary results show that the image reconstruction results obtained by the U-Net network are much better than that of the fully connected neural network algorithm, the traditional Linear back projection (LBP) algorithm and the Landweber iteration method.