TY - GEN
T1 - Image reconstruction method of electromagnetic tomography based on finite rate of innovation
AU - Huang, Guoxing
AU - Fu, Ning
AU - Zhang, Jingchao
AU - Qiao, Liyan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/22
Y1 - 2016/7/22
N2 - In order to improve the image reconstruction quality of electromagnetic tomography (EMT), a new EMT image reconstruction method based on finite rate of innovation (FRI) is put forward in this paper. The FRI sampling process is used to extract the feature information and combined a new measurement equation. Firstly, the conductivity distribution of the target object inside pipeline is modeled as an one-dimensional FRI signal, and then be sampled with polynomial reproducing kernel in FRI sampling framework. Then, more measurements are obtained from these samples, which generate a new mathematical model for EMT image reconstruction problem. Finally, the original image signal is recovered by solving a L0 norm optimization problem with matching pursuit (OMP) algorithm. Experiment result shows that the image error and correlation coefficient of reconstructed images by the proposed method are much better than corresponding indicators obtained by Linear back-projection (LBP), Landweber and Total variation (TV) regularization algorithm. So it is a kind of EMT image reconstruction method with high efficiency and accuracy, which also provides a new method and means for EMT research.
AB - In order to improve the image reconstruction quality of electromagnetic tomography (EMT), a new EMT image reconstruction method based on finite rate of innovation (FRI) is put forward in this paper. The FRI sampling process is used to extract the feature information and combined a new measurement equation. Firstly, the conductivity distribution of the target object inside pipeline is modeled as an one-dimensional FRI signal, and then be sampled with polynomial reproducing kernel in FRI sampling framework. Then, more measurements are obtained from these samples, which generate a new mathematical model for EMT image reconstruction problem. Finally, the original image signal is recovered by solving a L0 norm optimization problem with matching pursuit (OMP) algorithm. Experiment result shows that the image error and correlation coefficient of reconstructed images by the proposed method are much better than corresponding indicators obtained by Linear back-projection (LBP), Landweber and Total variation (TV) regularization algorithm. So it is a kind of EMT image reconstruction method with high efficiency and accuracy, which also provides a new method and means for EMT research.
KW - L0 norm
KW - electromagnetic tomography (EMT)
KW - finite rate Of innovation
KW - image reconstruction
KW - polynomial reproducing kernel
UR - https://www.scopus.com/pages/publications/84980317934
U2 - 10.1109/I2MTC.2016.7520485
DO - 10.1109/I2MTC.2016.7520485
M3 - 会议稿件
AN - SCOPUS:84980317934
T3 - Conference Record - IEEE Instrumentation and Measurement Technology Conference
BT - I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2016
Y2 - 23 May 2016 through 26 May 2016
ER -