An Image Reconstruction Algorithm for Electrical Capacitance Tomography Based on Robust Principle Component AnalysisReportar como inadecuado




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1

Key Laboratory of Condition Monitoring and Control for Power Plant Equipment, Ministry of Education, North China Electric Power University, Beijing 102206, China

2

Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China





*

Author to whom correspondence should be addressed.



Abstract Electrical capacitance tomography ECT attempts to reconstruct the permittivity distribution of the cross-section of measurement objects from the capacitance measurement data, in which reconstruction algorithms play a crucial role in real applications. Based on the robust principal component analysis RPCA method, a dynamic reconstruction model that utilizes the multiple measurement vectors is presented in this paper, in which the evolution process of a dynamic object is considered as a sequence of images with different temporal sparse deviations from a common background. An objective functional that simultaneously considers the temporal constraint and the spatial constraint is proposed, where the images are reconstructed by a batching pattern. An iteration scheme that integrates the advantages of the alternating direction iteration optimization ADIO method and the forward-backward splitting FBS technique is developed for solving the proposed objective functional. Numerical simulations are implemented to validate the feasibility of the proposed algorithm. View Full-Text

Keywords: electrical capacitance tomography; capacitance sensor; image reconstruction; inverse problem; robust principle component analysis; forward-backward splitting technique; alternating direction iteration optimization method electrical capacitance tomography; capacitance sensor; image reconstruction; inverse problem; robust principle component analysis; forward-backward splitting technique; alternating direction iteration optimization method





Autor: Jing Lei 1,* , Shi Liu 1, Xueyao Wang 2 and Qibin Liu 2

Fuente: http://mdpi.com/



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