CGAN-ECT: Tomography Image Reconstruction from Electrical Capacitance Measurements Using CGANs
This work addresses the need for fast and accurate image reconstruction in industrial Electrical Capacitance Tomography applications, representing an incremental improvement with a novel data representation.
The authors tackled the problem of reconstructing high-quality images from electrical capacitance measurements in tomography by proposing a CGAN-based model, achieving an average image correlation coefficient of over 99.3% and a relative error of about 0.07, outperforming traditional and other deep learning methods.
Due to the rapid growth of Electrical Capacitance Tomography (ECT) applications in several industrial fields, there is a crucial need for developing high quality, yet fast, methodologies of image reconstruction from raw capacitance measurements. Deep learning, as an effective non-linear mapping tool for complicated functions, has been going viral in many fields including electrical tomography. In this paper, we propose a Conditional Generative Adversarial Network (CGAN) model for reconstructing ECT images from capacitance measurements. The initial image of the CGAN model is constructed from the capacitance measurement. To our knowledge, this is the first time to represent the capacitance measurements in an image form. We have created a new massive ECT dataset of 320K synthetic image measurements pairs for training, and testing the proposed model. The feasibility and generalization ability of the proposed CGAN-ECT model are evaluated using testing dataset, contaminated data and flow patterns that are not exposed to the model during the training phase. The evaluation results prove that the proposed CGAN-ECT model can efficiently create more accurate ECT images than traditional and other deep learning-based image reconstruction algorithms. CGAN-ECT achieved an average image correlation coefficient of more than 99.3% and an average relative image error about 0.07.