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

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.(C) 2022 Elsevier Inc. All rights reserved.

Automated summary

This article introduces an imaging model that combines learned priors with measurement physics to improve the reconstruction quality in electrical capacitance tomography. By utilizing multi-fidelity deep learning and the half-quadratic optimization method, this method is able to capture spatial details of imaging objects and obtain high-quality solutions.