论文标题
CGAN-ECT：使用CGAN的电容测量的层析成像图像重建
CGAN-ECT: Tomography Image Reconstruction from Electrical Capacitance Measurements Using CGANs
论文作者
论文摘要
由于电容层析成像（ECT）应用在几个工业领域的快速增长，因此从原始电容测量中开发出高质量但快速的图像重建方法的需求。深度学习是一种有效的非线性映射工具，用于复杂功能，在包括电断层扫描在内的许多领域都流行了。在本文中，我们提出了一个条件生成对抗网络（CGAN）模型，用于从电容测量中重建ECT图像。 CGAN模型的初始图像是根据电容测量构建的。据我们所知，这是第一次以图像形式表示电容测量。我们创建了一个新的大规模ECT数据集，该数据集的320K合成图像测量对进行训练和测试所提出的模型。使用测试数据集，污染的数据和流动模式评估了提出的CGAN-ECT模型的可行性和概括能力，这些数据和流量模式在训练阶段未暴露于模型。评估结果证明，与传统和其他基于学习的图像重建算法相比，提出的CGAN-ECT模型可以有效地创建更准确的ECT图像。 CGAN-ECT达到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。
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.
