Image Reconstruction Algorithm Based on the Filled Function Method for Electrical Capacitance Tomography

Image reconstruction algorithms play a vital role in the successful applications of the electrical capacitance tomography (ECT). In this paper, a new objective functional, which unifies the regularized least squares (LS) method, the regularized total least squares (TLS) method, the regularized data least squares (DLS) method and the regularized generalized TLS method into a concise formulation, is proposed. The filled function method, which is coupled with the homotopy algorithm, is employed to solve the proposed objective functional. The proposed algorithm is tested by the noise-free capacitance data and the noise-contaminated capacitance data. Excellent numerical performances and encouraging results are observed. In the cases of the reconstructed objects considered in this paper, the spatial resolution and accuracy of the reconstructed images are markedly improved. In addition, the reconstructed results under the different noise levels reveal that the proposed algorithm is very competent to deal with the inaccurate nature in the capacitance data. As a result, a promising algorithm is introduced for ECT image reconstruction.Abstract-Image technique