| Issue |
MATEC Web Conf.
Volume 176, 2018
2018 6th International Forum on Industrial Design (IFID 2018)
|
|
|---|---|---|
| Article Number | 01031 | |
| Number of page(s) | 5 | |
| Section | Intelligent Design and Computer Technology | |
| DOI | https://doi.org/10.1051/matecconf/201817601031 | |
| Published online | 02 July 2018 | |
Reconstruction Method of Electrical Capacitance Tomography Based on Wavelet Fusion
1
Department of Automation, North China Electric Power University,
071003, China
2
Yunnan Electric Power Research Institute,
Kunming,
650217, China
3
College of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China
*
Corresponding author : Zhou Lei:zhouleiect@163.com
The accuracy of reconstructed images of Electrical Capacitance Tomography (ECT) is a bottleneck concerning the successful application of ECT. An image data fusion algorithm based on wavelet transform was proposed in this paper to improve the accuracy of reconstructed images. First, reconstructed images were obtained using conjugate gradient least square algorithm and Landweber iterative algorithm, respectively. Secondly, reconstructed images were decomposed by wavelet. After that, the approximate component was processed according to the weighted average fusion rule. The detail component was processed according to the maximum fusion rule of absolute value. Finally, the new reconstructed images were obtained by wavelet reconstruction. Simulation and static experimental results showed that the reconstructed images with higher accuracy can be obtained after fusion and the artefacts were decreased obviously.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.