Fine Structure Recognition in Multichannel Observations

0385357 - ÚTIA 2013 RIV US eng C - Conference Paper (international conference)
Šimberová, Stanislava - Haindl, Michal - Šroubek, Filip
Fine Structure Recognition in Multichannel Observations.
International Conference on Digital Image Computing Techniques and Applications (DICTA) 2012. Piscataway: IEEE Press, 2012, s. 1-7. ISBN 978-1-4673-2180-8.
[International Conference on Digital Image Computing Techniques and Applications (DICTA) 2012. Fremantle (AU), 03.12.2012-05.12.2012]
R&D Projects: GA ČR GAP103/11/1552; GA ČR GA102/08/1593; GA ČR GA102/08/0593
Grant - others:CESNET(CZ) 409/2011
Institutional support: RVO:67985556 ; RVO:67985815
Keywords : image restoration * image recognition
Subject RIV: BD - Theory of Information; BN - Astronomy, Celestial Mechanics, Astrophysics (ASU-R)
http://library.utia.cas.cz/separaty/2012/RO/haindl-fine structure recognition in multichannel observations.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0007445