A novel algorithm based on L1-Lp norm for inverse problem of electromagnetic tomography. (March 2019)

• Record Type: Journal Article
• Authors: Liu, Xianglong; Liu, Ze
• Is Part Of: Flow measurement and instrumentation. Volume 65(2019)
• Page Start: 318
• Page End: 326
• Publication Date: 2019-03
• Subjects: Electromagnetic tomography -- Image reconstruction -- L1-Lp norm -- Inverse problem; Fluid dynamic measurements -- Periodicals; Flow meters -- Periodicals; Fluides, Dynamique des -- Mesure -- Périodiques; Débitmètres -- Périodiques; 681.2805
• DOI: 10.1016/j.flowmeasinst.2019.01.010
• Languages: English
• ISSNs: 0955-5986

Abstract

Abstract: Electromagnetic tomography (EMT) is a novel imaging modality of electrical tomography, which appears to be very promising. The precision and imaging speed of image reconstruction algorithms of EMT are the keys to its application in industrial and biomedical fields. Image reconstruction in EMT is a typical ill-posed and ill-conditioned inverse problem. Following analyzing the advantages and disadvantages of traditional Tikhonov regularization algorithm and total variation algorithm, a new objective functional is introduced with L 1 norm on the data term and L p norm on the regularization term in this paper, which transforms the inverse problem of EMT into an optimization problem. Besides, the L 1 -L p optimization framework is solved by the approximation Gauss-Newton algorithm. Both numerical simulation and experimental results demonstrate that the proposed algorithm is capable of enhancing spatial resolution. It also proves that the proposed algorithm has better performance in terms of the typical patterns, compared with the traditional image reconstruction algorithms such as linear back projection (LBP), standard Tikhonov regularization algorithm and the projected Landweber iterative algorithm. Highlights: Novel image reconstruction algorithm for electromagnetic tomography is introduced. Optimizing p value keeps performance of the algorithm in optimal condition. Spatial resolution of the reconstructed image is enhanced. Performance of the algorithm is superior compared with the traditional algorithms.