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Vector-valued image interpolation by an anisotropic diffusion-projection PDE

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dc.contributor.author Roussos, A en
dc.contributor.author Maragos, P en
dc.date.accessioned 2014-03-01T02:45:00Z
dc.date.available 2014-03-01T02:45:00Z
dc.date.issued 2007 en
dc.identifier.issn 03029743 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/32086
dc.subject Anisotropic Diffusion en
dc.subject Color Image en
dc.subject Image Interpolation en
dc.subject Interpolation Method en
dc.subject Nonlinear Diffusion en
dc.subject.other Anisotropy en
dc.subject.other Computational geometry en
dc.subject.other Interpolation en
dc.subject.other Mathematical models en
dc.subject.other Nonlinear analysis en
dc.subject.other Appropriate smoothing en
dc.subject.other Blurring en
dc.subject.other Diffusion flow en
dc.subject.other Vector-valued images en
dc.subject.other Image analysis en
dc.title Vector-valued image interpolation by an anisotropic diffusion-projection PDE en
heal.type conferenceItem en
heal.identifier.primary 10.1007/978-3-540-72823-8_10 en
heal.identifier.secondary http://dx.doi.org/10.1007/978-3-540-72823-8_10 en
heal.publicationDate 2007 en
heal.abstract We propose a nonlinear image interpolation method, based on an anisotropic diffusion PDE and designed for the general case of vector-valued images, The interpolation solution is restricted to the subspace of functions that can recover the discrete input image, after an appropriate smoothing and sampling, The proposed nonlinear diffusion flow lies on this subspace and its strength and anisotropy effectively adapt to the local variations and geometry of image structures, The derived model efficiently reconstructs the real image structures, leading to a natural interpolation, with reduced blurring, staircase and ringing artifacts of classic methods. This method also outperforms other existing PDE-based interpolation methods. We present experimental results that prove the potential and efficacy of the method as applied to graylevel and color images. © Springer-Verlag Berlin Heidelberg 2007. en
heal.journalName Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) en
dc.identifier.doi 10.1007/978-3-540-72823-8_10 en
dc.identifier.volume 4485 LNCS en
dc.identifier.spage 104 en
dc.identifier.epage 115 en


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