Abstract
A new image reconstruction algorithm for electrical capacitance tomography based on iterative weighted fidelity and hybrid regularization is proposed. Specifically, an improved cost function model with the weighted data fidelity term and non-convex regularization term can better describe the sparsity of images and strengthen the anti-noises ability compared with the L1-norm or L2-norm regularization methods. In order to solve the proposed model efficiently, the alternating direction method of multipliers is used to divide the complex optimization problem into several simple iterative sub-problems. Moreover, the iterative shrinkage thresholding algorithm and the iterative p-shrinkage algorithm are also adopted to solve the sub-problems. Besides, simulation and experiments for different permittivity distributions are investigated with noise-free and noise-contaminated cases respectively. The research results verify that reconstruction images with the proposed algorithm have fewer artifacts and deformations, clearer edges, and better noise robustness than the other methods considered.
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