测量原理-数据-领域知识融合ECT重建方法

Measurement Principles-data-domain Knowledge Fusion Reconstruction Method for ECT

低质量的图像制约了电容层析成像在多相流参数测量中的应用。针对该问题,引入了由深度卷积神经网络预测的数据驱动先验,提出了融合测量原理、数据驱动先验和稀疏先验的成像模型;建立了新的算法实现成像模型的高效求解。评估结果证实,与其他的成像算法相比,新算法在细节重建、伪影去除和鲁棒性等方面具有显著优势。

Low-quality images constrain the application of electrical capacitance tomography in multiphase flow parameter measurements.Aiming at this problem,the data driven prior(DDP)predicted by a deep convolutional neural network was introduced,and a new imaging model fusing the measurement principle,the DDP and the sparsity prior was proposed.A new algorithm was established to achieve the efficient solution of the imaging model.Evaluation results confirm that the new algorithm has significant advantages over other imaging algorithms in terms of detail reconstruction,artifact removal and robustness.