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

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.