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International Conference on Scale Space and Variational Methods in Computer Vision

SSVM 2007: Scale Space and Variational Methods in Computer Vision pp 80–91Cite as

Best Basis Compressed Sensing

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4485))

Abstract

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.

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Author information

Authors and Affiliations

  1. Ceremade, Université Paris Dauphine, Place du Maréchal De Lattre De Tassigny, 75775 Paris Cedex 16, France

    Gabriel Peyré

Authors
  1. Gabriel Peyré
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Fiorella Sgallari Almerico Murli Nikos Paragios

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© 2007 Springer Berlin Heidelberg

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Peyré, G. (2007). Best Basis Compressed Sensing. In: Sgallari, F., Murli, A., Paragios, N. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2007. Lecture Notes in Computer Science, vol 4485. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72823-8_8

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  • DOI: https://doi.org/10.1007/978-3-540-72823-8_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72822-1

  • Online ISBN: 978-3-540-72823-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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