A Non-Convex L₁-Norm Penalty-Based Total Generalized Variation Model for Reconstruction of Conductivity Distribution

Abstract

Electrical impedance tomography (EIT) is a potential imaging technique for reconstructing the interior conductivity distribution within an object. The reconstruction is realized by processing the boundary voltage measured from the EIT. Mathematically, the reconstruction of conductivity distribution is a highly nonlinear ill-posed inverse problem as the solution is not unique and is sensitive to the noise, which largely hinders the practical application of the EIT technique. In this paper, a novel modified non-convex ${L}_{1}$ -norm penalty based total generalized variation (NCP-TGV) model is proposed to solve the inverse problem and reconstruct the conductivity distribution. An iteratively reweighted ${L}_{1}$ algorithm is developed to convert the non-convex model to convex function and the Chambolle-Pock primal-dual algorithm is then applied to solve the minimization problem. To demonstrate the performance of the proposed NCP-TGV model in reconstructing the conductivity distribution, extensive numerical simulation and experimental work have been carried out. Additionally, the proposed approach has been validated by comparing its performance with the convex ${L}_{1}$ -norm penalty based total variation (TV) and TGV inverse models. The results indicate that the images reconstructed by the proposed NCP-TGV model show better quality with the advantages of simultaneous reduction of staircase effect and preservation of edge in comparison to the TV and TGV approaches.