Implications for compressed sensing of a new sampling theorem on the sphere
Details
State: Public
Version: author
Serval ID
serval:BIB_3EC8833CE049
Type
Inproceedings: an article in a conference proceedings.
Publication sub-type
Abstract (Abstract): shot summary in a article that contain essentials elements presented during a scientific conference, lecture or from a poster.
Collection
Publications
Institution
Title
Implications for compressed sensing of a new sampling theorem on the sphere
Title of the conference
SPARS 2011, 4th Workshop on Signal Processing with Adaptive Sparse Structured Representations
Address
Edinburgh, Scotland, June 27-30, 2011
ISBN
9781471668692
Publication state
Published
Issued date
2011
Series
4th Workshop on Signal Processing with Adaptive Sparse Structured Representations
Pages
45
Language
english
Abstract
A sampling theorem on the sphere has been developed
recently, requiring half as many samples as alternative
equiangular sampling theorems on the sphere. A reduction
by a factor of two in the number of samples required to
represent a band-limited signal on the sphere exactly has
important implications for compressed sensing, both in
terms of the dimensionality and sparsity of signals. We
illustrate the impact of this property with an inpainting
problem on the sphere, where we show the superior
reconstruction performance when adopting the new sampling
theorem compared to the alternative.
recently, requiring half as many samples as alternative
equiangular sampling theorems on the sphere. A reduction
by a factor of two in the number of samples required to
represent a band-limited signal on the sphere exactly has
important implications for compressed sensing, both in
terms of the dimensionality and sparsity of signals. We
illustrate the impact of this property with an inpainting
problem on the sphere, where we show the superior
reconstruction performance when adopting the new sampling
theorem compared to the alternative.
Create date
07/01/2014 9:11
Last modification date
20/08/2019 14:35
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