Edge preserving image denoising in reproducing kernel Hilbert spaces

Pantelis Bouboulis, Sergios Theodoridis, Konstantinos Slavakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The goal of this paper is the development of a novel approach for the problem of Noise Removal, based on the theory of Reproducing Kernels Hilbert Spaces (RKHS). The problem is cast as an optimization task in a RKHS, by taking advantage of the celebrated semi-parametric Representer Theorem. Examples verify that in the presence of gaussian noise the proposed method performs relatively well compared to wavelet based technics and outperforms them significantly in the presence of impulse or mixed noise.

Original languageEnglish (US)
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Pages2660-2663
Number of pages4
DOIs
StatePublished - 2010
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: Aug 23 2010Aug 26 2010

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other2010 20th International Conference on Pattern Recognition, ICPR 2010
Country/TerritoryTurkey
CityIstanbul
Period8/23/108/26/10

Fingerprint

Dive into the research topics of 'Edge preserving image denoising in reproducing kernel Hilbert spaces'. Together they form a unique fingerprint.

Cite this