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

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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


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