A novel computational imaging algorithm based on split Bregman iterative for electrical capacitance tomography

Abstract

As an advanced detection technology in the industrial field, electrical capacitance tomography (ECT) can better reconstruct the material distribution state in the measured area by selecting the appropriate algorithm. In order to improve the reconstruction quality, this paper devises a novel objective function to model the ECT image reconstruction problem, in which L 1 -norm is deployed as data fidelity with the focus on weakening the influence of capacitance outliers on the reconstruction quality, L P regularization reinforces the sparseness of the phantom objects, and L 1 regularization applies to model deviation variables to increase the robustness of the system. Based on the fast-iterative shrinkage thresholding technique and the soft thresholding method as sub-solvers, the split Bregman iterative method is designed as an effective solver for the proposed objective function. Numerical simulation and experimental validate that the proposed algorithm has excellent imaging ability compared with other imaging algorithms, which provides a good choice for the practical application of ECT in the future.

A Novel Computational Imaging Algorithm for Electrical Capacitance Tomography

“…The TR theory usually transforms ill-posed problems into optimization problems to solve. Its objective function consists of two parts: the data fidelity term and the regularizer [18]. The generalized expression is as follows:…”

Measurement of Moisture Distribution in a Biomass Silo Through Electrical Capacitance Tomography

“…Image reconstruction algorithms are used to reconstruct the permittivity distribution in a biomass silo from the measured capacitance values. There are five commonly used algorithms for image reconstruction, i.e., the linear back projection (LBP) algorithm, the Tikhonov algorithm, the Landweber algorithm, the simultaneous iterative reconstruction technique (SIRT) and the fast iterative shrinkage thresholding (FSBI) [40].…”

“…These simulation results reconstructed with the FSBI algorithm are the best amongst the five algorithms. This is primarily due to the fact that L1 regularization improves the robustness of the FSBI algorithm, while Lp regularization enhances the sparsity of the targeted distributions [40].…”