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Statements

Subject Item
n2:RIV%2F67985815%3A_____%2F12%3A00385357%21RIV13-GA0-67985815
rdf:type
skos:Concept n10:Vysledek
dcterms:description
Two restoration methods applied to the multitemporal solar images are presented. Our main goal is to model and remove degradation in a subimage, where a specific event is investigated. Using information of the input (blurred) channels within a short observed sequence a new undegraded image is reconstructed. Degradation is assumed to follow a linear degradation model with an unknown possibly non-homogeneous point spread function (PSF) and additive noise. The first method ({/bf VAM}) is based on multichannel blind deconvolution (MBD) using a variational approach to blur estimation, while the second one ({/bf SAM}) supposes solution of the multidimensional causal regressive model representing the degraded image (channel). Experimental image data are from the ground based observation (white light) and satellite SOHO mission - EIT (EUV). Contributions of both suggested methods and their generalization are discussed. Two restoration methods applied to the multitemporal solar images are presented. Our main goal is to model and remove degradation in a subimage, where a specific event is investigated. Using information of the input (blurred) channels within a short observed sequence a new undegraded image is reconstructed. Degradation is assumed to follow a linear degradation model with an unknown possibly non-homogeneous point spread function (PSF) and additive noise. The first method ({/bf VAM}) is based on multichannel blind deconvolution (MBD) using a variational approach to blur estimation, while the second one ({/bf SAM}) supposes solution of the multidimensional causal regressive model representing the degraded image (channel). Experimental image data are from the ground based observation (white light) and satellite SOHO mission - EIT (EUV). Contributions of both suggested methods and their generalization are discussed.
dcterms:title
Fine Structure Recognition in Multichannel Observations Fine Structure Recognition in Multichannel Observations
skos:prefLabel
Fine Structure Recognition in Multichannel Observations Fine Structure Recognition in Multichannel Observations
skos:notation
RIV/67985815:_____/12:00385357!RIV13-GA0-67985815
n10:predkladatel
n11:ico%3A67985815
n4:aktivita
n15:P n15:I
n4:aktivity
I, P(GA102/08/0593), P(GA102/08/1593), P(GAP103/11/1552)
n4:dodaniDat
n7:2013
n4:domaciTvurceVysledku
n18:6117864
n4:druhVysledku
n19:D
n4:duvernostUdaju
n8:S
n4:entitaPredkladatele
n17:predkladatel
n4:idSjednocenehoVysledku
136597
n4:idVysledku
RIV/67985815:_____/12:00385357
n4:jazykVysledku
n20:eng
n4:klicovaSlova
image restoration; image recognition
n4:klicoveSlovo
n13:image%20restoration n13:image%20recognition
n4:kontrolniKodProRIV
[A21DFD658BA1]
n4:mistoKonaniAkce
Fremantle
n4:mistoVydani
Piscataway
n4:nazevZdroje
International Conference on Digital Image Computing Techniques and Applications (DICTA) 2012
n4:obor
n12:BN
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
3
n4:projekt
n14:GAP103%2F11%2F1552 n14:GA102%2F08%2F1593 n14:GA102%2F08%2F0593
n4:rokUplatneniVysledku
n7:2012
n4:tvurceVysledku
Haindl, Michal Šimberová, Stanislava Šroubek, Filip
n4:typAkce
n5:WRD
n4:zahajeniAkce
2012-12-03+01:00
s:numberOfPages
7
n21:hasPublisher
IEEE Press
n16:isbn
978-1-4673-2180-8