Computational Imaging Method with a Learned Plug-and-Play Prior for Electrical Capacitance Tomography

Abstract

Electrical capacitance tomography (ECT) is a potent image-based measurement technology for monitoring industrial processes, but low-quality images generally limit its application scope and measurement reliability. To increase the precision of reconstruction, in this study, a data-driven plug-and-play prior abstracted by a deep convolutional neural network (DCNN) and the sparseness prior of imaging objects, in form of regularizers, are jointly leveraged to generate a potent imaging model, in which the L1 norm of the mismatch error acts as a data fidelity term (DFT) to weaken the sensitivity of estimation result to noisy input data. The DCNN is embedded into the split Bregman (SB) technique to generate a powerful computing scheme for solving the built imaging model and the fast iterative shrinkage-thresholding algorithm (FISTA) is applied to solve the sub-problems efficiently. Extensive numerical results verify that the proposed imaging technique has competitive reconstruction ability and better robustness in comparison with the state-of-the-art methods. This study demonstrates the validity and efficacy of the proposed algorithm in reducing reconstruction error. Most importantly, the research outcomes verify that the data-driven plug-and-play prior and the sparseness prior can be jointly embedded into the imaging model, leading to a remarkable decline in reconstruction error.