"Fine Structure Recognition in Multichannel Observations" . . "3"^^ . "[A21DFD658BA1]" . "Fine Structure Recognition in Multichannel Observations" . "Fremantle" . "Haindl, Michal" . . "International Conference on Digital Image Computing Techniques and Applications (DICTA) 2012" . . "Piscataway" . "7"^^ . . "1"^^ . "2012-12-03+01:00"^^ . . . . "RIV/67985815:_____/12:00385357!RIV13-GA0-67985815" . . . "\u0160imberov\u00E1, Stanislava" . "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 . . . "Fine Structure Recognition in Multichannel Observations"@en . "RIV/67985815:_____/12:00385357" . "978-1-4673-2180-8" . "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." . . "I, P(GA102/08/0593), P(GA102/08/1593), P(GAP103/11/1552)" . . . . . . . "136597" . "IEEE Press" . "Fine Structure Recognition in Multichannel Observations"@en . "\u0160roubek, Filip" . "image restoration; image recognition"@en . .