Total variation denoising with iterated conditional expectation - Louchet - Workshop 2 - CEB T1 2019

Cécile Louchet (Univ. Orléans) / 12.03.2019 Total variation denoising with iterated conditional expectation. 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. ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook :   / instituthenripoincare   Twitter :   / inhenripoincare   Instagram :   / instituthenripoincare   ************************************* Langue : Anglais; Date : 12.03.2019; Conférencier : Louchet, Cécile; Évenement : Workshop 2 - CEB T1 2019; Lieu : IHP; Mots Clés : denoising, iterated conditional expectation (ICE), expectation a posteriori, total variation, deblurring