MATEC Web Conf.
Volume 252, 2019III International Conference of Computational Methods in Engineering Science (CMES’18)
|Number of page(s)||6|
|Section||Probability, Statistics Quality Control|
|Published online||14 January 2019|
Tomographic image correction with noise reduction algorithms
Lublin University of Technology, Faculty of Management, Nadbystrzycka 38, 20-618 Lublin, Poland
2 Research and Development Center, Netrix S.A., Związkowa 26, 20-148 Lublin, Poland
* Corresponding author: email@example.com
This article presents an original approach to improve the results of tomographic reconstructions by denoising the input data, which affects output images improving. The algorithms used in the research are based on autoencoders and Elastic Net - both related to artificial intelligence or machine-learning developed controllers. Due to the reduction of unnecessary features and removal of mutually correlated input variables generated by the tomography electrodes, good quality reconstructions of tomographic images were obtained. The simulation experiments proved that the presented methods could be effective in improving the quality of reconstructed tomographic images.
© The Authors, published by EDP Sciences, 2019
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (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.