Monte Carlo markov chain techniques for unsupervised MRF-based image denoising (Articolo in rivista)

Type

• Articolo in rivista (Classe)
• Prodotto della ricerca (Classe)

Label

• Monte Carlo markov chain techniques for unsupervised MRF-based image denoising (Articolo in rivista) (literal)

Anno

• 2003-01-01T00:00:00+01:00 (literal)

Alternative label

• Tonazzini A., Bedini L. (2003)
  Monte Carlo markov chain techniques for unsupervised MRF-based image denoising
  in Pattern recognition letters
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Tonazzini A., Bedini L. (literal)

Pagina inizio

• 55 (literal)

Pagina fine

• 64 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 24 (literal)

Rivista

• Pattern recognition letters (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note

• n. 1-3 A0-01 (literal)

Note

• ISI Web of Science (WOS) (literal)

Titolo

• Monte Carlo markov chain techniques for unsupervised MRF-based image denoising (literal)

Abstract

• This paper deals with discontinuity-adaptive smoothing for recovering degraded images,when Markov random ?eld models with explicit lines are used,but no a priori information about the free parameters of the related Gibbs distributions is available. The adopted approach is based on the maximization of the posterior distribution with respect to the line ?eld and the Gibbs parameters,while the intensity ?eld is assumed to be clamped to the maximizer of the posterior itself,conditioned on the lines and the parameters. This enables the application of a mixed-annealing algorithm for the maximum a posteriori (MAP) estimation of the image ?eld,and of Markov chain Monte Carlo techniques, over binary variables only, for the simultaneous maximum likelihood estimation of the parameters. A practical procedure is then derived which is nearly as fast as a MAP image reconstruction by mixed-annealing with known Gibbs parameters. We derive the method for the general case of a linear degradation process plus superposition of additive noise,and experimentally validate it for the sub-case of image denoising. (literal)

Prodotto di

• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)

Autore CNR

• ANNA TONAZZINI (Persona)
• LUIGI BEDINI (Unità di personale interno)

Incoming links:

Prodotto

• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)

Autore CNR di

• LUIGI BEDINI (Unità di personale interno)
• ANNA TONAZZINI (Persona)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Pattern recognition letters (Rivista)