Study On Imaging Method In Electrical Capacitance Tomography

The ECT technique belongs to a promising measurement method.It acquires the capacitance value between the electrode plates pairs outside the measurement area,and visually reconstructs the distribution of the permittivity in the measurement area according to the obtained capacitance value through an appropriate image reconstruction method.Thereby some process are assessed qualitatively and quantitatively.Due to its non-invasive measurement,low equipment cost,fast measurement and high security,the ECT system has been found numerous potential applications in variety of areas.This thesis mainly studies the ECT image algorithm,and the main work and achievements are as follows:(1)The conventional ECT imaging algorithms,such as the linear back projection method,the Tikhonov regularization algorithm,the Landweber iterative algorithm,the algebraic reconstruction technique,the simultaneous iterative reconstruction technique,the conjugate gradient method,and the Newton Raphson iteration method have been detailed analyzed and some numerical simulations are developed to study the numerical performance of the above algorithms.(2)An imaging algorithm based on sparse regularization of wavelet transform domain is proposed to improve the reconstruction quality.Within the framework of the Tikhonov regularization methodology and the transform-domain sparsity method,a new cost function encapsulating the wavelet based sparsity constraint is proposed to model the ECT imaging problem.An iteration scheme that integrates the superiorities of the alternating direction method of multipliers algorithm and splits a complicated optimization problem into several simpler sub-problems is developed to seek for the optimal solution of the proposed cost function.Numerical experiments validate that the proposed imaging algorithm is practicable and effective,and can improve the reconstruction accuracy and robustness.(3)Inspired by the memetic algorithm framework,a new imaging method is proposed to overcome the drawback of input data inaccuracy on imaging accuracy.Vary from the common imaging methods,in this section,a regularization-based loss function is proposed to transform the ECT imaging task into an optimization problem,in which the least trimmed squares method is employed to measure the data fidelity,and the Lp norm of the variables is used as the regularizer.An efficient memetic algorithm method that constitutes of the monkey king evolution algorithm and the Fletcher-Reeves conjugate gradient method is developed for solving the proposed loss function.The numerical simulations reveal that the feasibility and robustness of the proposed memetic algorithm lead to a series of satisfactory results in the ECT field.(4)In order to overcome the defects of single prior information and improve the quality of reconstructed images,this section proposes an image reconstruction algorithm based on multiple prior information.In practical applications,when the reconstruction target becomes complicated,the algorithm of single prior information often fails to obtain satisfactory results.Therefore,in this section a new cost function that simultaneously considers the sparsity and low rank properties of the imaging targets is proposed to improve the quality of the reconstruction images,in which the image reconstruction task is converted into an optimization problem.At the same time,under the framework of the split Bregman algorithm,an iterative scheme that splits a complicated optimization problem into several simpler sub-tasks is developed to solve the proposed cost function efficiently,in which the fast iterative shrinkage thresholding algorithm is introduced to accelerate the convergence.Numerical experiment results verify the effectiveness of the proposed algorithm on improving the reconstruction precision and robustness.Research findings improve the reconstruction quality of the result in the ECT field,and make contributions for the theory research of the multiphase flow parameter visualization measurement.