Treffer: Author manuscript, published in 'Scale Space and Variational Methods in Computer Vision (SSVM'07), Ischia: Italie (2007)' DOI:10.1007/978-3-540-72823-8_8 Best Basis Compressed Sensing
Title:
Author manuscript, published in 'Scale Space and Variational Methods in Computer Vision (SSVM'07), Ischia: Italie (2007)' DOI:10.1007/978-3-540-72823-8_8 Best Basis Compressed Sensing
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The Pennsylvania State University CiteSeerX Archives
Publication Year:
2009
Collection:
CiteSeerX
Document Type:
Fachzeitschrift
text
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application/pdf
Language:
English
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Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Accession Number:
edsbas.1F065F93
Database:
BASE
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This paper proposes an extension of compressed sensing that allows to express the sparsity prior in a dictionary of bases. This enables the use of the random sampling strategy of compressed sensing together with an adaptive recovery process that adapts the basis to the structure of the sensed signal. A fast greedy scheme is used during reconstruction to estimate the best basis using an iterative refinement. Numerical experiments on sounds and geometrical images show that adaptivity is indeed crucial to capture the structures of complex natural signals. 1