Imaging tasks most often require an energy minimization interpretable in a probabilistic approach as a maximum a posteriori. Taking instead the expectation a posteriori gives an interesting alternative but confronts the question of numerical integration in high dimension. We propose a variable-at-a-time integration, called after by iterated conditional expectation (ICE), that approximates the expectation a posteriori. We try it on total variation denoising for which it gives good visual properties and linear convergence. We give several clues concerning extensions of the method. Joint work with Lionel Moisan.
By Sylvain Lefèbvre
By Julien Rabin
By Ron Kimmel
By Michael Lindenbaum
By Cécile Louchet
By Michael Unser
By Hermine Biermé
By Pierre Chainais
By Jérémie Bigot
By Gersende Fort
By Remco Duits
By Stéphanie Allassonnière
By Charles Kervrann
By Xavier Descombes
By Marie-Paule Cani
By Irène Kaltenmark
By Joan Glaunès
By Marcelo Pereyra
By Alain Trouvé
