Research on optimization method of capacitance tomography based on contribution degree analysis

The Landweber algorithm is limited in its use in further applications due to its problems of semi-convergence and slow reconstruction speed. To solve the above issues, this paper first analyzes the causes of the semi-convergence characteristic of the Landweber algorithm from the perspective of the negative sensitivity field. Second, a method of data screening based on a contribution degree analysis is proposed, to weaken the influence of negative sensitivity fields on the semi-convergence characteristic of the algorithm. Then, based on this method, valid capacitance data are selected from the original capacitance data. Finally, the reconstructed results of the Landweber algorithm with the valid capacitance data and original capacitance data are evaluated, by taking the correlation coefficient and computation time as evaluation criteria. The results indicate that the new method effectively suppresses the semi-convergence characteristic of the algorithm, improves the convergence effect of the algorithm, and increases the image reconstruction quality and speed.