Data and measurement mechanism integrated imaging method for electrical capacitance tomography

Abstract

This study presents a new imaging paradigm for overcoming the challenges limiting the improvement of the imaging quality in the electrical capacitance technique. The new imaging model enables the integration of measurement physics, sparsity-induced prior and physics-informed multi-fidelity learning prior (PIMFLP), as well as the synergy between data-driven and measurement physics modeling paradigms. The transformed L1 norm is used to model the PIMFLP, and the maximum correntropy criterion is used as a data misfit term to restrict the adverse impact of noises or outliers. The PIMFLP is learned from data and characterizes the structural details of imaging targets. The half-quadratic optimization method is developed to overcome computational challenges in solving the novel imaging model. A new physics-informed multi-fidelity learning method is developed to predict PIMFLP by synergizing deep convolutional neural network with measurement physics, and a new bilevel optimization model solved by a new nested algorithm that merges the genetic algorithm and the split Bregman method is proposed for training. Comparisons with other popular reconstruction algorithms confirm that the new imaging method leads to the improved reconstruction accuracy and noise immunity, and opens up new possibilities for unlocking the potential of the measurement technology.