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Due to the rapid growth of Electrical Capacitance Tomography (ECT)applications in several industrial fields, there is a crucial need fordeveloping high quality, yet fast, methodologies of image reconstruction fromraw capacitance measurements. Deep learning, as an effective non-linear mappingtool for complicated functions, has been going viral in many fields includingelectrical tomography. In this paper, we propose a Conditional GenerativeAdversarial Network (CGAN) model for reconstructing ECT images from capacitancemeasurements. The initial image of the CGAN model is constructed from thecapacitance measurement. To our knowledge, this is the first time to representthe capacitance measurements in an image form. We have created a new massiveECT dataset of 320K synthetic image measurements pairs for training, andtesting the proposed model. The feasibility and generalization ability of theproposed CGAN-ECT model are evaluated using testing dataset, contaminated dataand flow patterns that are not exposed to the model during the training phase.The evaluation results prove that the proposed CGAN-ECT model can efficientlycreate more accurate ECT images than traditional and other deep learning-basedimage reconstruction algorithms. CGAN-ECT achieved an average image correlationcoefficient of more than 99.3% and an average relative image error about 0.07.

CGAN-ECT: Tomography Image Reconstruction from Electrical Capacitance Measurements Using CGANs