Detail publikace

Diffracted Image Restoration: A Machine learning approach

Originální název

Diffracted Image Restoration: A Machine learning approach

Anglický název

Diffracted Image Restoration: A Machine learning approach

Jazyk

en

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.

BibTex


@inproceedings{BUT102451,
  author="Vlastimil {Koudelka} and Zbyněk {Raida}",
  title="Diffracted Image Restoration: A Machine learning approach",
  annote="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.",
  address="COREP",
  booktitle="Proceedings of 2013 International Conference on Electromagnetics in Advanced Applications",
  chapter="102451",
  doi="10.1109/ICEAA.2013.6632375",
  howpublished="electronic, physical medium",
  institution="COREP",
  year="2013",
  month="september",
  pages="931--934",
  publisher="COREP",
  type="conference paper"
}