基于双参数模型的ECT图像重构混合算法

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Abstract To improve the image reconstruction speed and quality, a hybrid algorithm based on two-parameter model for electrical capacitance tomography(ECT) image reconstruction is proposed. Morozov discrepancy principle is used to choose Tikhonov regularization parameter which states that the regularization parameter should be chosen such that the error due to the regularization is equal to the error due to the observation data. To obtain an optimal regularization parameter rapidly, two-parameter model is derived based on Morozov discrepancy equation and further combined with Newton method. Simulation results show that the speed of image reconstruction is improved and the quality of the reconstructed image is better than other image reconstruction algorithms such as linear back-projection(LBP), Landweber algorithm and L-curve method.

Key words： Electrical capacitance tomography image reconstruction regularization parameter two-parameter model Morozov discrepancy principle hybrid algorithm

Received: 30 December 2011 Published: 20 March 2014

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TM930

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	Li Cuihuan
	Wang Youhua
	Geng Duyan
	Yang Xiaoguang

Cite this article:

Li Cuihuan,Wang Youhua,Geng Duyan等. Hybrid Algorithm Based on Two-parameter Model for Electrical Capacitance Tomography Image Reconstruction[J]. Transactions of China Electrotechnical Society, 2012, 27(4): 24-29.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2012/V27/I4/24

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