Study On Image Reconstruction Algorithms For Electrical Capacitance Tomography

Electrical capacitance tomography (ECT) is a visualized monitoring technique that expresses process characteristics responding to the phase distribution in the flow field as the form of image information, where the continuous and gradual changes of multi-phase dielectrics can cause the corresponding ones of the dielectric field between electrodes andAmong many image reconstruction methods. Tikhonov regularization algorithm is an efficient one and it has been applied in a lot of fields successfully. All in all, the Tikhonov regularization solution is a result balancing accuracy and stability of numerical values. This paper proposes a general Tikhonov functional based on the ill-posed nature of ECT image reconstruction; while the Newton arithmetic is used to solve the object functional conformated. At the same time, it is tested for the efficiency of algorithm and numerical performance by numerical emulation method. The results indicate that it has improved the quality of the image reconstruction observably and spatial resolution.In the existing ECT measurement methods, the dynamic information of reconstrcution object usually is ignored. So this paper presents a ensemble Kalman filter method based on prior information, which can take account of the dynamic information of reconstruction object in the process of image reconstruction through renewal and revision.In this paper, the information fusion method adopts the ensemble Kalman filter fusion algorithm for image reconstruction. The extend Tikhonov regularization algorithm achieves the steadiness of estimation and the stability of numerical solution via a novel steady functional. Then, the ensemble Kalman filter builds a estimation model for forecast using the prior information, and improves the quality of reconstructed images. As a remarkable result, the statistical estimation result is more easily obtained as the state space model. The ensemble Kalman filter is suboptimal estimator regularization where the error statistics are predicted by using an ensemble integration.Through the deduction, the extend Tikhonov regularization algorithm for image reconstruction is successfully applied to the image reconstruction of ECT system; through the finite element computation, the related image error evidently cuts down through the above algorithm from emulation.