Sparse image reconstruction on the sphere: implications of a new sampling theorem.

Détails

ID Serval
serval:BIB_F9746E79642E
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Sparse image reconstruction on the sphere: implications of a new sampling theorem.
Périodique
IEEE Transactions on Image Processing
Auteur(s)
McEwen J.D., Puy G., Thiran J.P., Vandergheynst P., Van De Ville D., Wiaux Y.
ISSN
1941-0042 (Electronic)
ISSN-L
1057-7149
Statut éditorial
Publié
Date de publication
2013
Volume
22
Numéro
6
Pages
2275-2285
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't Publication Status: ppublish
Résumé
We study the impact of sampling theorems on the fidelity of sparse image reconstruction on the sphere. We discuss how a reduction in the number of samples required to represent all information content of a band-limited signal acts to improve the fidelity of sparse image reconstruction, through both the dimensionality and sparsity of signals. To demonstrate this result, we consider a simple inpainting problem on the sphere and consider images sparse in the magnitude of their gradient. We develop a framework for total variation inpainting on the sphere, including fast methods to render the inpainting problem computationally feasible at high resolution. Recently a new sampling theorem on the sphere was developed, reducing the required number of samples by a factor of two for equiangular sampling schemes. Through numerical simulations, we verify the enhanced fidelity of sparse image reconstruction due to the more efficient sampling of the sphere provided by the new sampling theorem.
Pubmed
Web of science
Création de la notice
16/12/2013 10:34
Dernière modification de la notice
03/03/2018 22:54
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