Geometrical Constraints in Bayesian Wavelet Filtering of Images

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F04%3APU46947" target="_blank" >RIV/00216305:26220/04:PU46947 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Geometrical Constraints in Bayesian Wavelet Filtering of Images

Original language description

This paper describes a new method for the suppression of noise in images based on wavelet transform [3]. The method relies on two criteria. The first is a traditional criterion of smoothness of the image based on an approximation of the local Hőlder exponent via the wavelet coefficients. The second, novel criterion takes into account geometrical constraints, which are generally valid for natural and also simulated images. The smoothness measure and the geometrical constraints are combined in the describbed method in Bayesian probabilistic formulation, and are implemented as a Markov random field (MRF) image model. The manipulation of the wavelet coefficients is consequently based on the obtained probabilities. This method is proposed to quantitativelyimprove noise suppression comparing to classical methods based on wavelet transform. Qualitative improvement of images is also required (subjective sensation of sharpness and contrast).

Czech name

Geometrická omezení vlnkové fitrace obrazů založené na Bayesově přístupu

Czech description

This paper describes a new method for the suppression of noise in images based on wavelet transform [3]. The method relies on two criteria. The first is a traditional criterion of smoothness of the image based on an approximation of the local Hőlder exponent via the wavelet coefficients. The second, novel criterion takes into account geometrical constraints, which are generally valid for natural and also simulated images. The smoothness measure and the geometrical constraints are combined in the describbed method in Bayesian probabilistic formulation, and are implemented as a Markov random field (MRF) image model. The manipulation of the wavelet coefficients is consequently based on the obtained probabilities. This method is proposed to quantitativelyimprove noise suppression comparing to classical methods based on wavelet transform. Qualitative improvement of images is also required (subjective sensation of sharpness and contrast).

Type

D - Article in proceedings

CEP classification

JA - Electronics and optoelectronics

OECD FORD branch

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Project

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Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2004

Confidentiality

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

Article name in the collection

Proceeedings of the 10th Conference STUDENT EEICT 2004

ISBN

80-214-2635-7

ISSN

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e-ISSN

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Number of pages

5

Pages from-to

26-30

Publisher name

Neuveden

Place of publication

Brno

Event location

Brno

Event date

Apr 29, 2004

Type of event by nationality

CST - Celostátní akce

UT code for WoS article

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