TY - GEN
T1 - Total variation restoration of images corrupted by poisson noise with iterated conditional expectations
AU - Abergel, Rémy
AU - Louchet, Cécile
AU - Moisan, Lionel
AU - Zeng, Tieyong
N1 - Publisher Copyright:
© IFIP International Federation for Information Processing 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Fixedpoint algorithm
KW - Image denoising
KW - Incomplete gamma function
KW - Marginal conditional mean
KW - Poisson noise removal
KW - Posterior mean
KW - Staircasing effect
KW - Total variation
UR - http://www.scopus.com/inward/record.url?scp=84931024762&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-18461-6_15
DO - 10.1007/978-3-319-18461-6_15
M3 - Conference proceeding
AN - SCOPUS:84931024762
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 178
EP - 190
BT - Scale Space and Variational Methods in Computer Vision - 5th International Conference, SSVM 2015, Proceedings
A2 - Nikolova, Mila
A2 - Aujol, Jean-François
A2 - Papadakis, Nicolas
PB - Springer Verlag
T2 - 5th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2015
Y2 - 31 May 2015 through 4 June 2015
ER -