Variational multiframe restoration of images degraded by noisy (stochastic) blur kernels
Jung, Miyoun; Marquina, Antonio; Vese, Luminita A. (2013), Variational multiframe restoration of images degraded by noisy (stochastic) blur kernels, Journal of Computational and Applied Mathematics, 240, p. 123-124. http://dx.doi.org/10.1016/j.cam.2012.07.009
TypeArticle accepté pour publication ou publié
Journal nameJournal of Computational and Applied Mathematics
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Abstract (EN)This article introduces and explores a class of degradation models in which an image is blurred by a noisy (stochastic) point spread function (PSF). The aim is to restore a sharper and cleaner image from the degraded one. Due to the highly ill-posed nature of the problem, we propose to recover the image given a sequence of several observed degraded images or multiframes. Thus we adopt the idea of the multiframe approach introduced for image super-resolution, which reduces distortions appearing in the degraded images. Moreover, we formulate variational minimization problems with the robust (local or nonlocal) L1L1 edge-preserving regularizing energy functionals, unlike prior works dealing with stochastic point spread functions. Several experimental results on grey-scale/color images and on real static video data are shown, illustrating that the proposed methods produce satisfactory results. We also apply the degradation model to a segmentation problem with simultaneous image restoration.
Subjects / KeywordsImage restoration; Noisy blur kernel; Variational model; Total variation; Nonlocal method; Multiframe model
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