Parallel ProXimal Algorithm for image restoration using hybrid regularization - Archive ouverte HAL Access content directly
Journal Articles IEEE Transactions on Image Processing Year : 2011

Parallel ProXimal Algorithm for image restoration using hybrid regularization


Regularization approaches have demonstrated their effectiveness for solving ill-posed problems. However, in the context of variational restoration methods, a challenging question remains, which is how to find a good regularizer. While total variation introduces staircase effects, wavelet domain regularization brings other artefacts, e.g. ringing. However, a compromise can be found by introducing a hybrid regularization including several terms non necessarily acting in the same domain (e.g. spatial and wavelet transform domains). We adopt a convex optimization framework where the criterion to be minimized is split in the sum of more than two terms. For spatial domain regularization, isotropic or anisotropic total variation definitions using various gradient filters are considered. An accelerated version of the Parallel ProXimal Algorithm is proposed to perform the minimization. Some difficulties in the computation of the proximity operators involved in this algorithm are also addressed in this paper. Numerical experiments performed in the context of Poisson data recovery, show the good behavior of the algorithm as well as promising results concerning the use of hybrid regularization techniques.
Fichier principal
Vignette du fichier
0911.1536v2.pdf (522.66 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-00826121 , version 1 (26-05-2013)



Nelly Pustelnik, Caroline Chaux, Jean-Christophe Pesquet. Parallel ProXimal Algorithm for image restoration using hybrid regularization. IEEE Transactions on Image Processing, 2011, 20 (6), pp.2450-2462. ⟨10.1109/TIP.2011.2128335⟩. ⟨hal-00826121⟩
211 View
335 Download



Gmail Facebook X LinkedIn More