Name
Title | ||
|---|---|---|
Improving image reconstruction in electrical capacitance tomography based on deep learning |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IST48021.2019.9010087 | 2019 IEEE International Conference on Imaging Systems and Techniques (IST) |
Keywords | DocType | ISSN |
Electrical capacitance tomography,neural networks,enhancement of image quality | Conference | 1558-2809 |
ISBN | Citations | PageRank |
978-1-7281-3869-5 | 0 | 0.34 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hai Zhu | 1 | 87 | 22.69 |
| Jiangtao Sun | 2 | 3 | 4.80 |
| Lijun Xu | 3 | 85 | 44.81 |
| Shijie Sun | 4 | 106 | 16.25 |