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Photon-limited image denoising by inference on multiscale models

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dc.contributor.author Lefkimmiatis, S en
dc.contributor.author Papandreou, G en
dc.contributor.author Maragos, P en
dc.date.accessioned 2014-03-01T02:45:44Z
dc.date.available 2014-03-01T02:45:44Z
dc.date.issued 2008 en
dc.identifier.issn 15224880 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/32356
dc.subject Bayesian inference en
dc.subject Expectation-Maximization algorithm en
dc.subject Hidden Markov tree en
dc.subject Photon-limited imaging en
dc.subject Poisson en
dc.subject.other Bayesian inference en
dc.subject.other Expectation-Maximization algorithm en
dc.subject.other Hidden Markov tree en
dc.subject.other Photon-limited imaging en
dc.subject.other Poisson en
dc.subject.other Algorithms en
dc.subject.other Bayesian networks en
dc.subject.other Edge detection en
dc.subject.other Image segmentation en
dc.subject.other Imaging systems en
dc.subject.other Inference engines en
dc.subject.other Maximum likelihood estimation en
dc.subject.other Maximum principle en
dc.subject.other Mixtures en
dc.subject.other Optimization en
dc.subject.other Photons en
dc.subject.other Poisson equation en
dc.subject.other Poisson distribution en
dc.title Photon-limited image denoising by inference on multiscale models en
heal.type conferenceItem en
heal.identifier.primary 10.1109/ICIP.2008.4712259 en
heal.identifier.secondary http://dx.doi.org/10.1109/ICIP.2008.4712259 en
heal.identifier.secondary 4712259 en
heal.publicationDate 2008 en
heal.abstract We present an improved statistical model of Poisson processes, with applications to photon-limited imaging. We build on previous work, adopting a multiscale representation of the Poisson process in which the ratios of the underlying Poisson intensities (rates) in adjacent scales are modeled as mixtures of conjugate parametric distributions. Our main novel contributions are (1) a rigorous and robust regularized Expectation-Maximization (EM) algorithm for maximum-likelihood estimation of the rate-ratio density parameters directly from the observed Poisson data (counts); (2) extension of the method to work under a scale-recursive Hidden Markov Tree model (HMT) which couples the mixture label assignments in consecutive scales, thus modeling inter-scale coefficient dependencies in the vicinity of edges; and (3) exploration of a fully 2-D quad-tree image partitioning, involving Dirichlet-mixture rate-ratio densities, instead of the conventional separable binary image partitioning involving Beta-mixture rate-ratio densities. Experimental intensity estimation results on standard images with artificially simulated Poisson noise and photon-limited images with real shot noise demonstrate the effectiveness of the proposed approach. © 2008 IEEE. en
heal.journalName Proceedings - International Conference on Image Processing, ICIP en
dc.identifier.doi 10.1109/ICIP.2008.4712259 en
dc.identifier.spage 2332 en
dc.identifier.epage 2335 en


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