TY - GEN
T1 - Improving image reconstruction in electrical capacitance tomography based on deep learning
AU - Zhu, Hai
AU - Sun, Jiangtao
AU - Xu, Lijun
AU - Sun, Shijie
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Electrical capacitance tomography (ECT) has been developed for many years and made great progresses. Successful applications of ECT depend on the accuracy and speed of image reconstruction. In this paper, we propose a new method to enhance the quality of reconstructed image based on deep learning. Our method mainly applies to the images that have been reconstructed by conventional methods, such as Landweber iteration. In order to better measure the image quality, we introduce a set of evaluation criteria, including pixel accuracy, mean pixel accuracy, mean intersection over union and frequency weighted intersection over union. In test study, 5000 frames of simulation data containing three typical flow patterns were used. Results show that our method can give more accurate ECT images.
AB - Electrical capacitance tomography (ECT) has been developed for many years and made great progresses. Successful applications of ECT depend on the accuracy and speed of image reconstruction. In this paper, we propose a new method to enhance the quality of reconstructed image based on deep learning. Our method mainly applies to the images that have been reconstructed by conventional methods, such as Landweber iteration. In order to better measure the image quality, we introduce a set of evaluation criteria, including pixel accuracy, mean pixel accuracy, mean intersection over union and frequency weighted intersection over union. In test study, 5000 frames of simulation data containing three typical flow patterns were used. Results show that our method can give more accurate ECT images.
KW - Electrical capacitance tomography
KW - enhancement of image quality
KW - neural networks
UR - https://www.scopus.com/pages/publications/85081994887
U2 - 10.1109/IST48021.2019.9010087
DO - 10.1109/IST48021.2019.9010087
M3 - 会议稿件
AN - SCOPUS:85081994887
T3 - IST 2019 - IEEE International Conference on Imaging Systems and Techniques, Proceedings
BT - IST 2019 - IEEE International Conference on Imaging Systems and Techniques, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Imaging Systems and Techniques, IST 2019
Y2 - 8 December 2019 through 10 December 2019
ER -