Result Details
Diffracted Image Restoration: A Machine learning approach
KOUDELKA, V.; DEL RIO BOCIO, C.; RAIDA, Z. Diffracted Image Restoration: A Machine learning approach. In Proceedings of 2013 International Conference on Electromagnetics in Advanced Applications. Torino, Italy: COREP, 2013. p. 931-934. ISBN: 978-1-4673-5705-0.
Type
conference paper
Language
English
Authors
Koudelka Vlastimil, Ing., Ph.D., REL-SIX (FEEC), UREL (FEEC)
DEL RIO BOCIO, C.
Raida Zbyněk, prof. Dr. Ing., REL-SIX (FEEC), UREL (FEEC)
DEL RIO BOCIO, C.
Raida Zbyněk, prof. Dr. Ing., REL-SIX (FEEC), UREL (FEEC)
Abstract
Image restoration issues are closely connected with imaging systems, where image resolution is limited by diffraction phenomenon. The presented work is motivated by the super acuity of the Human vision, where the restoration step is implemented by some kind of parallel processor unit - neural network. The de-convolution process is formulated as a machine learning problem and the inverse operator is interpreted as a connectionist model.
Keywords
Diffraction, Image restoration, Imaging, Noise, Sensors, Stability analysis, Training
Published
2013
Pages
931–934
Proceedings
Proceedings of 2013 International Conference on Electromagnetics in Advanced Applications
Conference
International Conference on Electromagnetics in Advanced Applications (ICEAA) 2013
ISBN
978-1-4673-5705-0
Publisher
COREP
Place
Torino, Italy
DOI
BibTeX
@inproceedings{BUT102451,
author="KOUDELKA, V. and DEL RIO BOCIO, C. and RAIDA, Z.",
title="Diffracted Image Restoration: A Machine learning approach",
booktitle="Proceedings of 2013 International Conference on Electromagnetics in Advanced Applications",
year="2013",
pages="931--934",
publisher="COREP",
address="Torino, Italy",
doi="10.1109/ICEAA.2013.6632375",
isbn="978-1-4673-5705-0"
}
Departments