InforLorV4, Main, Exploration, bibRecord, 000E33

Sparsity-based edge noise removal from bilevel graphical document images

Identifieur interne : 000E33 ( Main/Exploration ); précédent : 000E32; suivant : 000E34

Sparsity-based edge noise removal from bilevel graphical document images

Auteurs : Thai V. Hoang [France] ; Elisa H. Barney Smith [États-Unis] ; Salvatore Tabbone [France]

Source :

International journal on document analysis and recognition : (Print) [ 1433-2833 ] ; 2014.

RBID : Pascal:14-0199553

Descripteurs français

Pascal (Inist)
- Détection contour, Image binaire, Représentation graphique, Dictionnaire, Restauration image, Image floue, Image bruitée, Vision ordinateur, Document graphique, Récupération, Niveau bruit, Régularité, Rapport signal bruit, Réduction bruit, Représentation parcimonieuse, Cognition spatiale, Résultat expérimental, Modélisation.
Wicri :
- topic : Dictionnaire.

English descriptors

KwdEn :
- Binary image, Blurred image, Computer vision, Dictionaries, Edge detection, Experimental result, Graphic document, Graphics, Image restoration, Modeling, Noise level, Noise reduction, Noisy image, Recovery, Regularity, Signal to noise ratio, Sparse representation, Spatial cognition.
mix :
- dictionary learning, directional denoising, sparse representation.

Abstract

This paper presents a new method to remove edge noise from graphical document images using geometrical regularities of the graphics contours that exist in the images. Denoising is understood as a recovery problem and is accomplished by employing a sparse representation framework in the form of a basis pursuit denoising algorithm. Directional information of the graphics contours is encoded by atoms in an overcomplete dictionary which is designed to match the input data. The optimal precision parameter used in this framework is shown to have a linear relationship with the level of the noise that exists in the image. Experimental results show the superiority of the proposed method over existing ones in terms of image recovery and contour raggedness.

Url:

https://hal.inria.fr/hal-00852418

Affiliations:

Le document en format XML

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<front><div type="abstract" xml:lang="en">This paper presents a new method to remove edge noise from graphical document images using geometrical regularities of the graphics contours that exist in the images. Denoising is understood as a recovery problem and is accomplished by employing a sparse representation framework in the form of a basis pursuit denoising algorithm. Directional information of the graphics contours is encoded by atoms in an overcomplete dictionary which is designed to match the input data. The optimal precision parameter used in this framework is shown to have a linear relationship with the level of the noise that exists in the image. Experimental results show the superiority of the proposed method over existing ones in terms of image recovery and contour raggedness.</div>
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Sparsity-based edge noise removal from bilevel graphical document images

Sparsity-based edge noise removal from bilevel graphical document images

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

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