Sparsity in Bayesian Blind Source Separation and Deconvolution

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F13%3A00396464" target="_blank" >RIV/67985556:_____/13:00396464 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-642-40991-2_35" target="_blank" >http://dx.doi.org/10.1007/978-3-642-40991-2_35</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-40991-2_35" target="_blank" >10.1007/978-3-642-40991-2_35</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sparsity in Bayesian Blind Source Separation and Deconvolution

Popis výsledku v původním jazyce

Blind source separation algorithms are based on various separation criteria. Differences in convolution kernels of the sources are common assumptions in audio and image processing. Since it is still an ill posed problem, any additional information is beneficial. In this contribution, we investigate the use of sparsity criteria for both the source signal and the convolution kernels. A probabilistic model of the problem is introduced and its Variational Bayesian solution derived. The sparsity of the solution is achieved by introduction of unknown variance of the prior on all elements of the convolution kernels and the mixing matrix. Properties of the model are analyzed on simulated data and compared with state of the art methods. Performance of the algorithm is demonstrated on the problem of decomposition of a sequence of medical data. Specifically, the assumption of sparseness is shown to suppress artifacts of unconstrained separation method.

Název v anglickém jazyce

Sparsity in Bayesian Blind Source Separation and Deconvolution

Popis výsledku anglicky

Blind source separation algorithms are based on various separation criteria. Differences in convolution kernels of the sources are common assumptions in audio and image processing. Since it is still an ill posed problem, any additional information is beneficial. In this contribution, we investigate the use of sparsity criteria for both the source signal and the convolution kernels. A probabilistic model of the problem is introduced and its Variational Bayesian solution derived. The sparsity of the solution is achieved by introduction of unknown variance of the prior on all elements of the convolution kernels and the mixing matrix. Properties of the model are analyzed on simulated data and compared with state of the art methods. Performance of the algorithm is demonstrated on the problem of decomposition of a sequence of medical data. Specifically, the assumption of sparseness is shown to suppress artifacts of unconstrained separation method.

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

<a href="/cs/project/GA13-29225S" target="_blank" >GA13-29225S: Slepá dekonvoluce obrazu v limitních podmínkách</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

Machine Learning and Knowledge Discovery in Databases

ISBN

978-3-642-40990-5

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

16

Strana od-do

548-563

Název nakladatele

Springer

Místo vydání

Berlin Heidelberg

Místo konání akce

Praha

Datum konání akce

24. 9. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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