Publication detail

Image Extrapolation Using K-SVD Algorithm

ŠPIŘÍK, J. ZÁTYIK, J. LÓCSI, L.

Original Title

Image Extrapolation Using K-SVD Algorithm

English Title

Image Extrapolation Using K-SVD Algorithm

Type

conference paper

Language

en

Original Abstract

This article presents a novel algorithm for image extrapolation, which utilizies K-SVD algorithm for training the overcomplete dictionary. With the trained dictionary we can get the extrapolated part of the image. The main idea is to perform the extrapolation for only one row or column at a time, and to drop some constraints on the overlapping of the patches. Our proposed algorithm is compared with the algorithm for image inpainting which can be also used for image extrapolation (with some special conditions). At the end some examples of extrapolated images are shown.

English abstract

This article presents a novel algorithm for image extrapolation, which utilizies K-SVD algorithm for training the overcomplete dictionary. With the trained dictionary we can get the extrapolated part of the image. The main idea is to perform the extrapolation for only one row or column at a time, and to drop some constraints on the overlapping of the patches. Our proposed algorithm is compared with the algorithm for image inpainting which can be also used for image extrapolation (with some special conditions). At the end some examples of extrapolated images are shown.

Keywords

Extrapolation, border extension, border prediction, image processing, K-SVD, sparse

RIV year

2013

Released

03.07.2013

ISBN

978-1-4799-0402-0

Book

Proceedings of the 36th International Conference on Telecommunications and Signal Processing

Pages from

877

Pages to

880

Pages count

4

Documents

BibTex


@inproceedings{BUT101114,
  author="Jan {Špiřík} and Ján {Zátyik} and Levente {Lócsi}",
  title="Image Extrapolation Using K-SVD Algorithm",
  annote="This article presents a novel algorithm for image extrapolation, which utilizies K-SVD algorithm for training the overcomplete dictionary. With the trained dictionary we can get the extrapolated part of the image. The main idea is to perform the extrapolation for only one row or column at a time, and to drop some constraints on the overlapping of the patches. Our proposed algorithm is compared with the algorithm for image inpainting which can be also used for image extrapolation (with some special conditions). At the end some examples of extrapolated images are shown.",
  booktitle="Proceedings of the 36th International Conference on Telecommunications and Signal Processing",
  chapter="101114",
  howpublished="electronic, physical medium",
  year="2013",
  month="july",
  pages="877--880",
  type="conference paper"
}