Learned prior-guided algorithm for flow field visualization in electrical capacitance tomography

Abstract

Electrical capacitance tomography provides great potential advantages in measuring flow field parameters by providing information about spatial-temporal medium distributions, but it is plagued by low-quality reconstructions. In order to overcome this challenge, in this work, the learned prior (LP) that bridges the measurement physics and data-driven modeling paradigms is introduced and coupled with the measurement physics and the domain knowledge into a novel imaging model for reshaping the tomographic reconstruction problem. The LP captures spatial details of imaging objects and guides the search to discover high-quality solutions. A new multi-fidelity deep learning is developed to predict the LP by using deep convolutional encoder-decoder network and multi-fidelity samples, which reduces the difficulty and cost of collecting high-fidelity samples. The established imaging model is solved within the framework of the half-quadratic optimization method. This work transforms the image reconstruction paradigm by fusing the measurement physics and data-driven modeling paradigms. The assessment results have validated that this novel method provides a range of advantages over popular methods, including higher reconstruction quality (RQ) and better robustness.