Name
Abstract | ||
|---|---|---|
Recently, methods based on Non-Local Total Variation (NLTV) minimization have become popular in image processing. They play a prominent role in a variety of applications such as denoising, compressive sensing, and inverse problems in general. In this work, we extend the NLTV framework by using some information divergences to build new sparsity measures for signal recovery. This leads to a general convex formulation of optimization problems involving information divergences. We address these problems by means of fast parallel proximal algorithms. In denoising and deconvolution examples, our approach is compared with ℓ2-NLTV based approaches. The proposed approach applies to a variety of other inverse problems. |
Year | Venue | Keywords |
|---|---|---|
2013 | Signal Processing Conference | deconvolution,inverse problems,minimisation,signal denoising,deconvolution,denoising,fast parallel proximal algorithms,general convex formulation,information divergences,information measures,inverse problems,nonlocal total variation minimization,optimization problems,proximal approach,signal recovery,sparsity measures,Divergences,convex optimization,inverse problems,non-local processing,parallel algorithms,proximity operator,total variation |
Field | DocType | Citations |
Mathematical optimization,Parallel algorithm,Image processing,Deconvolution,Minification,Inverse problem,Convex optimization,Optimization problem,Compressed sensing,Mathematics | Conference | 2 |
PageRank | References | Authors |
0.36 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mireille El Gheche | 1 | 14 | 6.39 |
| Anna Jezierska | 2 | 67 | 8.26 |
| Jean-Christophe Pesquet | 3 | 206 | 22.24 |
| Joumana Farah | 4 | 158 | 17.80 |