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Fine Structure Recognition in Multichannel Observations
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SYSNO ASEP 0385357 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Fine Structure Recognition in Multichannel Observations Author(s) Šimberová, Stanislava (ASU-R) RID
Haindl, Michal (UTIA-B) RID, ORCID
Šroubek, Filip (UTIA-B) RID, ORCIDNumber of authors 3 Source Title International Conference on Digital Image Computing Techniques and Applications (DICTA) 2012. - Piscataway : IEEE Press, 2012 - ISBN 978-1-4673-2180-8 Pages s. 1-7 Number of pages 7 s. Publication form Print - P Action International Conference on Digital Image Computing Techniques and Applications (DICTA) 2012 Event date 03.12.2012-05.12.2012 VEvent location Fremantle Country AU - Australia Event type WRD Language eng - English Country US - United States Keywords image restoration ; image recognition Subject RIV BD - Theory of Information Subject RIV - cooperation Astronomical Institute - Astronomy, Celestial Mechanics, Astrophysics R&D Projects GAP103/11/1552 GA ČR - Czech Science Foundation (CSF) GA102/08/1593 GA ČR - Czech Science Foundation (CSF) GA102/08/0593 GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 ; ASU-R - RVO:67985815 UT WOS 000316318400076 DOI 10.1109/DICTA.2012.6411740 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2013
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