Detail publikačního výsledku

Sparse image extrapolation using different inpainting algorithms

ŠPIŘÍK, J.; ZÁTYIK, J.

Originální název

Sparse image extrapolation using different inpainting algorithms

Anglický název

Sparse image extrapolation using different inpainting algorithms

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

Image inpainting via approximately solving underdetermined systems of linear equations can take different forms. State of the art methods use sparse solutions of such systems to inpaint (i.e. fill-in) the missing part of an image. Some of these approaches are applicable for image extrapolation as well, but this cannot be seen just as a special case of standard inpainting problem. For example, usual methods assume filling the holes from different directions, which is not tractable in the case of extrapolation. In this paper some of the algorithms that are tailored to inpainting are introduced and modified for use in image extrapolation. We use K-SVD algorithm that trains a dictionary for optimal sparse representation, MCA (Morphological Component Analysis) that expects two incoherent dictionaries for representing separately cartoon and texture. The last algorithm present is the statistics-based EM (Expectation Maximization). The performance of these algorithms for image extrapolation is compared on real images.

Anglický abstrakt

Image inpainting via approximately solving underdetermined systems of linear equations can take different forms. State of the art methods use sparse solutions of such systems to inpaint (i.e. fill-in) the missing part of an image. Some of these approaches are applicable for image extrapolation as well, but this cannot be seen just as a special case of standard inpainting problem. For example, usual methods assume filling the holes from different directions, which is not tractable in the case of extrapolation. In this paper some of the algorithms that are tailored to inpainting are introduced and modified for use in image extrapolation. We use K-SVD algorithm that trains a dictionary for optimal sparse representation, MCA (Morphological Component Analysis) that expects two incoherent dictionaries for representing separately cartoon and texture. The last algorithm present is the statistics-based EM (Expectation Maximization). The performance of these algorithms for image extrapolation is compared on real images.

Klíčová slova

image extrapolation, sparse, K-SVD, MCA, EM

Klíčová slova v angličtině

image extrapolation, sparse, K-SVD, MCA, EM

Autoři

ŠPIŘÍK, J.; ZÁTYIK, J.

Rok RIV

2014

Vydáno

12.09.2012

ISBN

978-80-554-0569-8

Kniha

Proceedings of the 14th International Conference on Research in Telecommunication Technologies

Edice

1

Strany od

247

Strany do

251

Strany počet

5

BibTex

@inproceedings{BUT94299,
  author="Jan {Špiřík} and Ján {Zátyik}",
  title="Sparse image extrapolation using different inpainting algorithms",
  booktitle="Proceedings of the 14th International Conference on Research in Telecommunication Technologies",
  year="2012",
  series="1",
  number="1",
  pages="247--251",
  isbn="978-80-554-0569-8"
}