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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F06%3A00118120%21RIV11-GA0-21230___
rdf:type
n8:Vysledek skos:Concept
dcterms:description
It is generally known, that the image usually contains a noise, which induces a degradation of image. We have a lot of methods, more or less suitable, for his removing. For the first time we can divide this method like a linear and nonlinear. To linear methods mainly belongs in spatial domain, convolutions filtering and the frequency mask in spectral domain. Nowadays is popular to use Discrete Wavelet Transform (DWT), because this transform is a very good tool for denoising. Two methods will be discussed in this paper using two types of Wavelet Transform. The first of them is based on feasible thresholding (hard or soft) of wavelet coefficients on a suitable decomposition level (see [1]). This method uses Wavelet Transform, which is usually named dyadic decomposition. The second one, more sophisticated, uses special type of Wavelet Transform - the steerable pyramid. The estimation of the image is proceeded using Bayesian least square estimator. It is generally known, that the image usually contains a noise, which induces a degradation of image. We have a lot of methods, more or less suitable, for his removing. For the first time we can divide this method like a linear and nonlinear. To linear methods mainly belongs in spatial domain, convolutions filtering and the frequency mask in spectral domain. Nowadays is popular to use Discrete Wavelet Transform (DWT), because this transform is a very good tool for denoising. Two methods will be discussed in this paper using two types of Wavelet Transform. The first of them is based on feasible thresholding (hard or soft) of wavelet coefficients on a suitable decomposition level (see [1]). This method uses Wavelet Transform, which is usually named dyadic decomposition. The second one, more sophisticated, uses special type of Wavelet Transform - the steerable pyramid. The estimation of the image is proceeded using Bayesian least square estimator.
dcterms:title
Removing Noise from an Imaging Data Removing Noise from an Imaging Data
skos:prefLabel
Removing Noise from an Imaging Data Removing Noise from an Imaging Data
skos:notation
RIV/68407700:21230/06:00118120!RIV11-GA0-21230___
n6:aktivita
n11:P
n6:aktivity
P(GA102/05/2054)
n6:dodaniDat
n16:2011
n6:domaciTvurceVysledku
n10:8430578
n6:druhVysledku
n21:D
n6:duvernostUdaju
n20:S
n6:entitaPredkladatele
n12:predkladatel
n6:idSjednocenehoVysledku
497196
n6:idVysledku
RIV/68407700:21230/06:00118120
n6:jazykVysledku
n15:eng
n6:klicovaSlova
discrete wavelet transform
n6:klicoveSlovo
n13:discrete%20wavelet%20transform
n6:kontrolniKodProRIV
[805A24DD1ED6]
n6:mistoKonaniAkce
Praha
n6:mistoVydani
Praha
n6:nazevZdroje
Proceedings of Workshop 2006
n6:obor
n7:JA
n6:pocetDomacichTvurcuVysledku
1
n6:pocetTvurcuVysledku
1
n6:projekt
n9:GA102%2F05%2F2054
n6:rokUplatneniVysledku
n16:2006
n6:tvurceVysledku
Švihlík, Jan
n6:typAkce
n14:EUR
n6:zahajeniAkce
2006-02-20+01:00
s:numberOfPages
2
n17:hasPublisher
České vysoké učení technické v Praze
n4:isbn
80-01-03439-9
n19:organizacniJednotka
21230