Robust Multichannel Blind Deconvolution via Fast Alternating Minimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F12%3A00376080" target="_blank" >RIV/67985556:_____/12:00376080 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/TIP.2011.2175740" target="_blank" >http://dx.doi.org/10.1109/TIP.2011.2175740</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TIP.2011.2175740" target="_blank" >10.1109/TIP.2011.2175740</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Robust Multichannel Blind Deconvolution via Fast Alternating Minimization

Popis výsledku v původním jazyce

Blind deconvolution, which comprises simultaneous blur and image estimations, is a strongly ill-posed problem. It is by now well known that if multiple images of the same scene are acquired, this multichannel (MC) blind deconvolution problem is better posed and allows blur estimation directly fromthe degraded images.We improve theMC idea by adding robustness to noise and stability in the case of large blurs or if the blur size is vastly overestimated. We formulate blind deconvolution as an?regularized optimization problemand seek a solution by alternately optimizing with respect to the image and with respect to blurs. Each optimization step is converted to a constrained problem by variable splitting and then is addressed with an augmented Lagrangian method, which permits simple and fast implementation in the Fourier domain. The rapid convergence of the proposed method is illustrated on synthetically blurred data. Applicability is also demonstrated on the deconvolution of real photos ta

Název v anglickém jazyce

Robust Multichannel Blind Deconvolution via Fast Alternating Minimization

Popis výsledku anglicky

Blind deconvolution, which comprises simultaneous blur and image estimations, is a strongly ill-posed problem. It is by now well known that if multiple images of the same scene are acquired, this multichannel (MC) blind deconvolution problem is better posed and allows blur estimation directly fromthe degraded images.We improve theMC idea by adding robustness to noise and stability in the case of large blurs or if the blur size is vastly overestimated. We formulate blind deconvolution as an?regularized optimization problemand seek a solution by alternately optimizing with respect to the image and with respect to blurs. Each optimization step is converted to a constrained problem by variable splitting and then is addressed with an augmented Lagrangian method, which permits simple and fast implementation in the Fourier domain. The rapid convergence of the proposed method is illustrated on synthetically blurred data. Applicability is also demonstrated on the deconvolution of real photos ta

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Image Processing

ISSN

1057-7149

e-ISSN

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Svazek periodika

21

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

1687-1700

Kód UT WoS článku

000302181800022

EID výsledku v databázi Scopus

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