Iterative weighted fidelity and hybrid regularization-based algorithm for ECT

Abstract

A new image reconstruction algorithm for electrical capacitance tomography (ECT) based on iterative weighted fidelity and hybrid regularization is proposed. Specifically, an improved cost function model including iterative weighted data fidelity term and hybrid regularization term can reduce the impact of the outlier and enhance the sparsity of solution. In order to solve the proposed model efficiently, the alternating direction method of multipliers (ADMM) is used to divide the complex optimization problem into several simple iterative sub-problems. Moreover, the iterative shrinkage thresholding algorithm (ISTA) and the iterative p-shrinkage algorithm (IPSA) are also adopted to solve the sub-problems. Besides, simulation experiments for six different permittivity distributions are investigated with noise-free and noise-contaminated cases respectively. The research results verify that the reconstruction images with proposed algorithm have less artifacts and deformations, clearer edges, and better noise robustness than that of the classic methods.