Detail publikačního výsledku

Diffracted Image Restoration: A Machine learning approach

KOUDELKA, V.; DEL RIO BOCIO, C.; RAIDA, Z.

Originální název

Diffracted Image Restoration: A Machine learning approach

Anglický název

Diffracted Image Restoration: A Machine learning approach

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova

Diffraction, Image restoration, Imaging, Noise, Sensors, Stability analysis, Training

Klíčová slova v angličtině

Diffraction, Image restoration, Imaging, Noise, Sensors, Stability analysis, Training

Autoři

KOUDELKA, V.; DEL RIO BOCIO, C.; RAIDA, Z.

Rok RIV

2014

Vydáno

09.09.2013

Nakladatel

COREP

Místo

Torino, Italy

ISBN

978-1-4673-5705-0

Kniha

Proceedings of 2013 International Conference on Electromagnetics in Advanced Applications

Strany od

931

Strany do

934

Strany počet

4

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"
}