Publication detail

Sparse image extrapolation using different inpainting algorithms

ŠPIŘÍK, J. ZÁTYIK, J.

Original Title

Sparse image extrapolation using different inpainting algorithms

English Title

Sparse image extrapolation using different inpainting algorithms

Type

conference paper

Language

en

Original Abstract

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.

English abstract

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.

Keywords

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

RIV year

2012

Released

12.09.2012

ISBN

978-80-554-0569-8

Book

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

Edition

1

Edition number

1

Pages from

247

Pages to

251

Pages count

5

Documents

BibTex


@inproceedings{BUT94299,
  author="Jan {Špiřík} and Ján {Zátyik}",
  title="Sparse image extrapolation using different inpainting algorithms",
  annote="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.",
  booktitle="Proceedings of the 14th International Conference on Research in Telecommunication Technologies",
  chapter="94299",
  edition="1",
  howpublished="electronic, physical medium",
  year="2012",
  month="september",
  pages="247--251",
  type="conference paper"
}