Reversible interpolation of vectorial images by an anisotropic diffusion-projection PDE

Roussos, A; Maragos, P

dc.contributor.author	Roussos, A	en
dc.contributor.author	Maragos, P	en
dc.date.accessioned	2014-03-01T01:31:48Z
dc.date.available	2014-03-01T01:31:48Z
dc.date.issued	2009	en
dc.identifier.issn	0920-5691	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19934
dc.subject	Anti-aliasing filter	en
dc.subject	Diffusion-projection PDE	en
dc.subject	Image interpolation	en
dc.subject	Nonlinear anisotropic diffusion	en
dc.subject	Partial differential equations (PDEs)	en
dc.subject	Reversibility	en
dc.subject	Sampling	en
dc.subject	Vector-valued images	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.other	Anti-aliasing filter	en
dc.subject.other	Image interpolation	en
dc.subject.other	Nonlinear anisotropic diffusion	en
dc.subject.other	Reversibility	en
dc.subject.other	Vector-valued images	en
dc.subject.other	Aircraft engines	en
dc.subject.other	Anti-aliasing	en
dc.subject.other	Computational fluid dynamics	en
dc.subject.other	Computer vision	en
dc.subject.other	Diffusion	en
dc.subject.other	Image analysis	en
dc.subject.other	Image segmentation	en
dc.subject.other	Nonlinear equations	en
dc.subject.other	Optical anisotropy	en
dc.subject.other	Partial differential equations	en
dc.subject.other	Interpolation	en
dc.title	Reversible interpolation of vectorial images by an anisotropic diffusion-projection PDE	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s11263-008-0132-x	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s11263-008-0132-x	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	In this paper, a nonlinear model for the interpolation of vector-valued images is proposed. This model is based on an anisotropic diffusion PDE and performs an interpolation that is reversible. 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 while its strength and anisotropy adapt to the local variations and geometry of image structures. The derived method effectively reconstructs the real image structures and yields a satisfactory interpolation result. Compared to classic and other existing PDE-based interpolation methods, our proposed method seems to increase the accuracy of the result and to reduce the undesirable artifacts, such as blurring, ringing, block effects and edge distortion. We present extensive experimental results that demonstrate the potential of the method as applied to graylevel and color images. © 2008 Springer Science+Business Media, LLC.	en
heal.publisher	SPRINGER	en
heal.journalName	International Journal of Computer Vision	en
dc.identifier.doi	10.1007/s11263-008-0132-x	en
dc.identifier.isi	ISI:000266477100002	en
dc.identifier.volume	84	en
dc.identifier.issue	2	en
dc.identifier.spage	130	en
dc.identifier.epage	145	en