A cross-validatory statistical approach to scale selection for image denoising by nonlinear diffusion

Papandreou, G; Maragos, P

dc.contributor.author	Papandreou, G	en
dc.contributor.author	Maragos, P	en
dc.date.accessioned	2014-03-01T02:43:03Z
dc.date.available	2014-03-01T02:43:03Z
dc.date.issued	2005	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31196
dc.subject	Computer Vision	en
dc.subject	Cross Validation	en
dc.subject	Diffusion Process	en
dc.subject	image denoising	en
dc.subject	Natural Images	en
dc.subject	Nonlinear Diffusion	en
dc.subject	Scale Space	en
dc.subject	Statistical Approach	en
dc.subject	Statistical Model	en
dc.subject.other	Acoustic noise	en
dc.subject.other	Algorithms	en
dc.subject.other	Computer vision	en
dc.subject.other	Mathematical models	en
dc.subject.other	Problem solving	en
dc.subject.other	Statistical methods	en
dc.subject.other	Image denoising	en
dc.subject.other	Nonlinear diffusion	en
dc.subject.other	Robust algorithms	en
dc.subject.other	Image analysis	en
dc.title	A cross-validatory statistical approach to scale selection for image denoising by nonlinear diffusion	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/CVPR.2005.21	en
heal.identifier.secondary	http://dx.doi.org/10.1109/CVPR.2005.21	en
heal.identifier.secondary	1467326	en
heal.publicationDate	2005	en
heal.abstract	Scale-spaces induced by diffusion processes play an important role in many computer vision tasks. Automatically selecting the most appropriate scale for a particular problem is a central issue for the practical applicability of such scale-space techniques. This paper concentrates on automatic scale selection when nonlinear diffusion scale-spaces are utilized for image denoising. The problem is studied in a statistical model selection framework and cross-validation techniques are utilized to address it in a principled way. The proposed novel algorithms do not require knowledge of the noise variance and have acceptable computational cost. Extensive experiments on natural images show that the proposed methodology leads to robust algorithms, which out-perform existing techniques for a wide range of noise types and noise levels. © 2005 IEEE.	en
heal.journalName	Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005	en
dc.identifier.doi	10.1109/CVPR.2005.21	en
dc.identifier.volume	I	en
dc.identifier.spage	625	en
dc.identifier.epage	630	en