A Novel Image Reconstruction Algorithm for Electrical Capacitance Tomography

Abstract

An algorithm to reconstruct images with Least Squares Support Vector Machines (LS-SVM) and Simulated Annealing Particle Swarm Optimization (APSO) is provided, which is named as SAP. In order to overcome the soft field characteristics of ECT sensitivity field, we exercised some image samples of typical flow pattern with LS-SVM so as to predict the capacitance error caused by the soft field characteristics and then construct the fitness function of the particle swarm optimization on basis of the capacitance error. This algorithm introduces simulated annealing ideas into PSO, adopts cooling process functions to replace the inertia weight function and construct the time variant inertia weight function featured in annealing mechanism, takes use of the APSO algorithm to search for the optimized resolution of Electrical Capacitance Tomography (ECT) reconstruction image. The simulation results show that SAP algorithm is featured in quick convergence rate and higher imaging precision. Compared with Land Weber algorithm and Newton-Raphson algorithm, the quality of reconstruction image with SAP is significantly improved.