Publication detail

Image Extrapolation using sparse methods

ŠPIŘÍK, J. ZÁTYIK, J.

Original Title

Image Extrapolation using sparse methods

English Title

Image Extrapolation using sparse methods

Type

journal article - other

Language

en

Original Abstract

Image extrapolation is the specific application in image processing. You have to extrapolate the image for example when you want to process the given image piecewise. When the border patches are incompleted you must extrapolate them to the given size. Nowadays,some basic extrapolations, e.g. linear, polynomial etc. are used. The advanced methods are presented in this paper. We are using the algorithms that are based on finding the sparse solutions in underdetermined systems of linear equations. Three algorithms are presented for image extrapolation. First one is the K-SVD algorithm. K-SVD is the algorithm that trains a dictionary which allows the optimal sparse representation. Second one is Morphological Component Analysis (MCA) which is based on Independent Component Analysis (ICA). The last is the Expectation Maximization (EM) algorithm. This algorithm is statistics-based. These three algorithms for image extrapolation are compared on the real images.

English abstract

Image extrapolation is the specific application in image processing. You have to extrapolate the image for example when you want to process the given image piecewise. When the border patches are incompleted you must extrapolate them to the given size. Nowadays,some basic extrapolations, e.g. linear, polynomial etc. are used. The advanced methods are presented in this paper. We are using the algorithms that are based on finding the sparse solutions in underdetermined systems of linear equations. Three algorithms are presented for image extrapolation. First one is the K-SVD algorithm. K-SVD is the algorithm that trains a dictionary which allows the optimal sparse representation. Second one is Morphological Component Analysis (MCA) which is based on Independent Component Analysis (ICA). The last is the Expectation Maximization (EM) algorithm. This algorithm is statistics-based. These three algorithms for image extrapolation are compared on the real images.

Keywords

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

RIV year

2013

Released

03.06.2013

Publisher

EDIS - Publishing Institution of Zilina University

Location

Zilina

ISBN

1335-4205

Periodical

Communications

Year of study

2013

Number

2a

State

SK

Pages from

174

Pages to

179

Pages count

6

Documents

BibTex


@article{BUT100541,
  author="Jan {Špiřík} and Ján {Zátyik}",
  title="Image Extrapolation using sparse methods",
  annote="Image extrapolation is the specific application in image processing. You have to extrapolate the image for example when you want to process the given image piecewise. When the border patches are incompleted you must extrapolate them to the given size. Nowadays,some basic extrapolations, e.g. linear, polynomial etc. are used. The advanced methods are presented in this paper. We are using the algorithms that are based on finding the sparse solutions in underdetermined systems of linear equations. Three algorithms are presented for image extrapolation. First one is the K-SVD algorithm. K-SVD is the algorithm that trains a dictionary which allows the optimal sparse representation. Second one is Morphological Component Analysis (MCA) which is based on Independent Component Analysis (ICA). The last is the Expectation Maximization (EM) algorithm. This algorithm is statistics-based. These three algorithms for image extrapolation are compared on the real images.",
  address="EDIS - Publishing Institution of Zilina University",
  chapter="100541",
  institution="EDIS - Publishing Institution of Zilina University",
  number="2a",
  volume="2013",
  year="2013",
  month="june",
  pages="174--179",
  publisher="EDIS - Publishing Institution of Zilina University",
  type="journal article - other"
}