Generalized flow pattern image reconstruction algorithm for electrical capacitance tomography

Successful applications of electrical capacitance tomography (ECT) depend on the speed and precision of the image reconstruction algorithms. In this paper, based on the semiparametric model, a generalized objective functional that considers the outliers in the measured capacitance data and the model error is proposed. A regularized combination minimax estimation is developed. An efficient algorithm, which integrates the advantages of the homotopy method where the homotopy equation is designed by the fixed-point homotopy and solved using the fixed-point iteration algorithm based on the alternate iteration scheme, the quantum particle swarm optimization algorithm that is coupled with the crossover and mutation operators, and the simulated annealing algorithm, is proposed. This algorithm is tested by the noise-free capacitance data and the noise-contaminated capacitance data, and encouraging results are observed. Numerical simulation results reveal the effectiveness and superiority of the proposed algorithm. In the cases of the reconstructed objects considered in this paper, the reconstructed results by the proposed algorithm show great improvement in the spatial resolution and accuracy. The spatial resolution of the reconstructed images is enhanced, and the artifacts in the reconstructed images can be removed effectively. Furthermore, the reconstructed results by the proposed algorithm under the noise-contaminated capacitance data reveal that the proposed algorithm is very competent to deal with the inaccurate nature in the capacitance data. Consequently, a promising algorithm is introduced for ECT image reconstruction.