Research Article
Polynomial Reproducing Kernel Based Image Reconstruction for ECT
@INPROCEEDINGS{10.1007/978-3-031-04245-4_40, author={Juntao Sun and Guoxing Huang and Qinfeng Li and Weidang Lu and Yu Zhang}, title={Polynomial Reproducing Kernel Based Image Reconstruction for ECT}, proceedings={6GN for Future Wireless Networks. 4th EAI International Conference, 6GN 2021, Huizhou, China, October 30--31, 2021, Proceedings}, proceedings_a={6GN}, year={2022}, month={5}, keywords={Image reconstruction Electrical capacitance tomography (ECT) Polynomial reproducing kernel L0 norm Finite rate of innovation (FRI)}, doi={10.1007/978-3-031-04245-4_40} }- Juntao Sun
Guoxing Huang
Qinfeng Li
Weidang Lu
Yu Zhang
Year: 2022
Polynomial Reproducing Kernel Based Image Reconstruction for ECT
6GN
Springer
DOI: 10.1007/978-3-031-04245-4_40
Abstract
Electrical capacitance tomography (ECT) is one of an electrical tomography technique, which is widely used in industrial process monitoring. It is based on multiphase flow detection and has been widely used in many fields in recent years. It is one of the research hot topics of process tomography. For purpose of improving the accuracy of ECT image reconstruction, a new image reconstruction method for ECT based on polynomial reproducing sampling kernel is put forward in this paper. Firstly, the grayscale of image is a Dirac pulse train, and it can be modeled into a discrete finite rate of innovation (FRI) model. Then, the feature information of polynomial is extracted by FRI sampling. Finally, a new observation equation is constructed by using the feature information. The original image was obtained by solving the L0 norm optimization problem. Simulations have shown that the image accuracy reconstructed by this method are better than existing algorithms.
