Fast edge preserving picture recovery by Finite Markov Random Fields

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

5 Citations (Scopus)

Abstract

We investigate the properties of edge preserving smoothing in the context of Finite Markov Random Fields (FMRF). Our main result follows from the definition of discontinuity adaptive potential for FMRF which imposes to penalize linearly image gradients. This is in agreement with the Total Variation based regularization approach to image recovery and analysis. We also report a fast computational algorithm exploiting the finiteness of the field, it uses integer arithmetic and a gradient descent updating procedure. Numerical results on real images and comparisons with anisotropic diffusion and half-quadratic regularization are reported.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages277-286
Number of pages10
Volume3617 LNCS
DOIs
Publication statusPublished - 1 Dec 2005
Externally publishedYes
Event13th International Conference on Image Analysis and Processing, ICIAP 2005 - Cagliari, Italy
Duration: 6 Sep 20058 Sep 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3617 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other13th International Conference on Image Analysis and Processing, ICIAP 2005
CountryItaly
CityCagliari
Period6/9/058/9/05

    Fingerprint

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Ceccarelli, M. (2005). Fast edge preserving picture recovery by Finite Markov Random Fields. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3617 LNCS, pp. 277-286). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3617 LNCS). https://doi.org/10.1007/11553595_34