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.

翻译：随着电容层析成像（ECT）技术在多个工业领域的快速应用，亟需开发既能保证高质量又能实现快速处理的原始电容测量图像重建方法。深度学习作为复杂函数的有效非线性映射工具，已在包括电容层析成像在内的众多领域广泛应用。本文提出了一种基于条件生成对抗网络（CGAN）的模型，用于从电容测量值重建ECT图像。该CGAN模型的初始图像由电容测量值构建。据我们所知，这是首次将电容测量值以图像形式呈现。我们创建了一个包含32万对合成图像测量值的大规模ECT数据集，用于训练和测试所提模型。通过测试数据集、受污染数据以及训练阶段未出现的流型，评估了所提CGAN-ECT模型的可行性与泛化能力。评估结果表明，与传统及其他基于深度学习的图像重建算法相比，CGAN-ECT模型能够更高效地生成更精确的ECT图像。CGAN-ECT的平均图像相关系数超过99.3%，平均相对图像误差约为0.07。