Document Type

Conference Paper

Department/Unit

Department of Mathematics

Title

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

Language

English

Abstract

© IFIP International Federation for Information Processing 2015. 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.

Keywords

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

Publication Date

2015

Source Publication Title

Scale space and variational methods in computer vision: 5th International Conference, SSVM 2015, Lège-Cap Ferret, France, May 31 - June 4, 2015, Proceedings

Start Page

178

End Page

190

Conference Location

Lège-Cap Ferret, France

Publisher

Springer International Publishing

DOI

10.1007/978-3-319-18461-6_15

ISSN (print)

03029743

ISBN (print)

9783319184609

ISBN (electronic)

9783319184616

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