Image denoising in nonlinear scale-spaces: Automatic scale selection via cross-validation

Papandreou, G; Maragos, P

dc.contributor.author	Papandreou, G	en
dc.contributor.author	Maragos, P	en
dc.date.accessioned	2014-03-01T02:43:22Z
dc.date.available	2014-03-01T02:43:22Z
dc.date.issued	2005	en
dc.identifier.issn	15224880	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31364
dc.subject	Cross Validation	en
dc.subject	Image Analysis	en
dc.subject	image denoising	en
dc.subject	Image Processing	en
dc.subject	Nonlinear Diffusion	en
dc.subject	Scale Space	en
dc.subject	Statistical Model	en
dc.subject.other	Automatic scale selection	en
dc.subject.other	Image denoising	en
dc.subject.other	Optimal scale selection	en
dc.subject.other	Algorithms	en
dc.subject.other	Calculations	en
dc.subject.other	Nonlinear systems	en
dc.subject.other	Optimal systems	en
dc.subject.other	Statistical methods	en
dc.subject.other	Image processing	en
dc.title	Image denoising in nonlinear scale-spaces: Automatic scale selection via cross-validation	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICIP.2005.1529792	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICIP.2005.1529792	en
heal.identifier.secondary	1529792	en
heal.publicationDate	2005	en
heal.abstract	Multiscale, i.e. scale-space image analysis is a powerful framework for many image processing tasks. A fundamental issue with such scale-space techniques is the automatic selection of the most salient scale for a particular application. This paper considers optimal scale selection when nonlinear diffusion and morphological scale-spaces are utilized for image denoising. The problem is studied from a statistical model selection viewpoint 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, have acceptable computational cost and are readily integrated with a wide class of scale-space inducing processes which require setting of a scale parameter. Our experiments show that this methodology leads to robust algorithms, which outperform existing scale-selection techniques for a wide range of noise types and noise levels. © 2005 IEEE.	en
heal.journalName	Proceedings - International Conference on Image Processing, ICIP	en
dc.identifier.doi	10.1109/ICIP.2005.1529792	en
dc.identifier.volume	1	en
dc.identifier.spage	481	en
dc.identifier.epage	484	en