Sparse image extrapolation using different inpainting algorithms

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Sparse image extrapolation using different inpainting algorithms

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Sparse image extrapolation using different inpainting algorithms

Popis výsledku anglicky

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

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

ISBN

978-80-554-0569-8

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

247-251

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

Vrátna, SK

Datum konání akce

1. 9. 2012

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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