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  • 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. (en)
Title
  • Fine Structure Recognition in Multichannel Observations
  • Fine Structure Recognition in Multichannel Observations (en)
skos:prefLabel
  • Fine Structure Recognition in Multichannel Observations
  • Fine Structure Recognition in Multichannel Observations (en)
skos:notation
  • RIV/67985815:_____/12:00385357!RIV13-GA0-67985815
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • I, P(GA102/08/0593), P(GA102/08/1593), P(GAP103/11/1552)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 136597
http://linked.open...ai/riv/idVysledku
  • RIV/67985815:_____/12:00385357
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • image restoration; image recognition (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [A21DFD658BA1]
http://linked.open...v/mistoKonaniAkce
  • Fremantle
http://linked.open...i/riv/mistoVydani
  • Piscataway
http://linked.open...i/riv/nazevZdroje
  • International Conference on Digital Image Computing Techniques and Applications (DICTA) 2012
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Haindl, Michal
  • Šroubek, Filip
  • Šimberová, Stanislava
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • IEEE Press
https://schema.org/isbn
  • 978-1-4673-2180-8
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