Total variation restoration of images corrupted by poisson noise with iterated conditional expectations

Rémy Abergel, Cécile Louchet, Lionel Moisan*, Tieyong ZENG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

13 Citations (Scopus)

Abstract

Interpreting the celebrated Rudin-Osher-Fatemi (ROF) model in a Bayesian framework has led to interesting new variants for Total Variation image denoising in the last decade. The Posterior Mean variant avoids the so-called staircasing artifact of the ROF model but is computationally very expensive. Another recent variant, called TV-ICE (for Iterated Conditional Expectation), delivers very similar images but uses a much faster fixed-point algorithm. In the present work, we consider the TV-ICE approach in the case of a Poisson noise model. We derive an explicit form of the recursion operator, and show linear convergence of the algorithm, as well as the absence of staircasing effect. We also provide a numerical algorithm that carefully handles precision and numerical overflow issues, and show experiments that illustrate the interest of this Poisson TV-ICE variant.

Original languageEnglish
Title of host publicationScale Space and Variational Methods in Computer Vision - 5th International Conference, SSVM 2015, Proceedings
EditorsMila Nikolova, Jean-François Aujol, Nicolas Papadakis
PublisherSpringer Verlag
Pages178-190
Number of pages13
ISBN (Electronic)9783319184609
DOIs
Publication statusPublished - 2015
Event5th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2015 - Lege-Cap Ferret, France
Duration: 31 May 20154 Jun 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9087
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2015
Country/TerritoryFrance
CityLege-Cap Ferret
Period31/05/154/06/15

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Fixedpoint algorithm
  • Image denoising
  • Incomplete gamma function
  • Marginal conditional mean
  • Poisson noise removal
  • Posterior mean
  • Staircasing effect
  • Total variation

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