Image Reconstruction of Electrical Capacitance Tomography Based on Adaptive Support Driven Bayesian Reweighted Algorithm

• Published in: IEEE sensors journal Vol. 21; no. 18; pp. 20648 - 20656
• Main Authors: Zhang, Lifeng, Dai, Li
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: adaptive support driven Bayesian reweighted algorithm; Algorithms; Bayes methods; Bayesian analysis; Capacitance; electrical capacitance tomography; Feature extraction; Flow distribution; Image quality; Image reconstruction; Imaging; Inverse problems; Iterative algorithms; Lasso; Machine learning; Permittivity; Sensors; Signal processing algorithms; sparse Bayesian learning; Tomography
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2021.3099241

Image reconstruction of electrical capacitance tomography (ECT) is a nonlinear and ill-posed inverse problem. Therefore, how to introduce an effective algorithm to reduce the ill conditioned degree of ECT imaging, thereby improving the imaging accuracy is an important subject of ECT algorithm research. In order to further study the subject, a novel ECT image reconstruction algorithm based on an adaptive support driven Bayesian reweighted (ASDBR) algorithm was proposed in this paper. The great advantage of this algorithm is that it can accurately extract the main features of the flow pattern and remove redundant information. This algorithm transforms the original problem into a series of subproblems with iteratively reweighted weights, and solves these subproblems by the iterative shrinkage-thresholding algorithm (ISTA). Comparisons are made among the ASDBR algorithm, the Landweber iterative algorithm, the sparse Bayesian learning (SBL) algorithm, and Lasso. Both simulation and experiment results show that the proposed new method considerably enhances the quality of the reconstructed image.