An image reconstruction algorithm based on the semiparametric model for electrical capacitance tomography

Electrical capacitance tomography (ECT) is considered as a promising tomography technology, and exactly reconstructing the original objects is highly desirable in real applications. In this paper, a generalized image reconstruction model that simultaneously considers the inaccurate property in the measured capacitance data and the linearization approximation error is presented. A generalized objective function, which has been developed using a combinational M-estimation and an extended stabilizing item, is proposed. The objective function unifies six estimation methods into a concise formula, where different estimation methods can be easily obtained by selecting different parameters. The homotopy method that integrates the beneficial advantages of the alternant iteration scheme is employed to solve the proposed objective function. Numerical simulations are implemented to evaluate the numerical performances and effectiveness of the proposed algorithm, and the numerical results reveal that the proposed algorithm is efficient and overcomes the numerical instability in the process of ECT image reconstruction. For the reconstructed objects in this paper, a dramatic improvement in accuracy and spatial resolution can be achieved, which indicates that the proposed algorithm is a promising candidate for solving ECT inverse problems.