Mathematics – Optimization and Control
Scientific paper
2009-11-08
Mathematics
Optimization and Control
Scientific paper
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.
Chaux Caroline
Pesquet Jean-Christophe
Pustelnik Nelly
No associations
LandOfFree
Parallel Proximal Algorithm for Image Restoration Using Hybrid Regularization -- Extended Version does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Parallel Proximal Algorithm for Image Restoration Using Hybrid Regularization -- Extended Version, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Parallel Proximal Algorithm for Image Restoration Using Hybrid Regularization -- Extended Version will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-686485