Poisson-haar transform: A nonlinear multiscale representation for photon-limited image denoising

Lefkimmiatis, S; Papandreou, G; Maragos, P

dc.contributor.author	Lefkimmiatis, S	en
dc.contributor.author	Papandreou, G	en
dc.contributor.author	Maragos, P	en
dc.date.accessioned	2014-03-01T02:46:23Z
dc.date.available	2014-03-01T02:46:23Z
dc.date.issued	2009	en
dc.identifier.issn	15224880	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32655
dc.subject	Bayesian estimation	en
dc.subject	Haar wavelet transform	en
dc.subject	Hidden Markov tree	en
dc.subject	Photon-limited imaging	en
dc.subject	Poisson noise	en
dc.subject.other	Bayesian estimations	en
dc.subject.other	Haar wavelet transform	en
dc.subject.other	Hidden Markov tree	en
dc.subject.other	Photon-limited imaging	en
dc.subject.other	Poisson noise	en
dc.subject.other	Decomposition	en
dc.subject.other	Estimation	en
dc.subject.other	Image processing	en
dc.subject.other	Imaging systems	en
dc.subject.other	Photons	en
dc.subject.other	Signal processing	en
dc.subject.other	Wavelet decomposition	en
dc.subject.other	Bayesian networks	en
dc.title	Poisson-haar transform: A nonlinear multiscale representation for photon-limited image denoising	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICIP.2009.5414053	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICIP.2009.5414053	en
heal.identifier.secondary	5414053	en
heal.publicationDate	2009	en
heal.abstract	We present a novel multiscale image representation belonging to the class of multiscale multiplicative decompositions, which we term Poisson-Haar transform. The proposed representation is well-suited for analyzing images degraded by signal-dependent Poisson noise, allowing efficient estimation of their underlying intensity by means of multiscale Bayesian schemes. The Poisson-Haar decomposition has a direct link to the standard 2-D Haar wavelet transform, thus retaining many of the properties that have made wavelets successful in signal processing and analysis. The practical relevance and effectiveness of the proposed approach is verified through denoising experiments on simulated and real-world photon-limited images. ©2009 IEEE.	en
heal.journalName	Proceedings - International Conference on Image Processing, ICIP	en
dc.identifier.doi	10.1109/ICIP.2009.5414053	en
dc.identifier.spage	3853	en
dc.identifier.epage	3856	en