MRL-filters: a general class of nonlinear systems and their optimal design for image processing

Pessoa, L; Maragos, P

dc.contributor.author	Pessoa, L	en
dc.contributor.author	Maragos, P	en
dc.date.accessioned	2014-03-01T01:46:50Z
dc.date.available	2014-03-01T01:46:50Z
dc.date.issued	1998	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/25057
dc.subject	Image Processing	en
dc.subject	Learning Process	en
dc.subject	Linear Filtering	en
dc.subject	lms algorithm	en
dc.subject	Nonlinear System	en
dc.subject	Optimal Design	en
dc.subject	Steepest Descent Method	en
dc.subject	Adaptive Filter	en
dc.subject	Filter Design	en
dc.subject	Image Restoration	en
dc.subject	Indexing Terms	en
dc.subject	Least Mean Square	en
dc.subject	Nonlinear Filter	en
dc.subject	Optimal Filtering	en
dc.subject	Signal Processing	en
dc.subject	System Identification	en
dc.subject	Training Algorithm	en
dc.title	MRL-filters: a general class of nonlinear systems and their optimal design for image processing	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/83.701150	en
heal.identifier.secondary	http://dx.doi.org/10.1109/83.701150	en
heal.publicationDate	1998	en
heal.abstract	A class of morphological/rank/linear (MRL)-filters is presented as a general nonlinear tool for image processing. They consist of a linear combination between a morphological/rank filter and a linear filter. A gradient steepest descent method is proposed to optimally design these filters, using the averaged least mean squares (LMS) algorithm. The filter design is viewed as a learning process, and convergence	en
heal.journalName	IEEE Transactions on Image Processing	en
dc.identifier.doi	10.1109/83.701150	en