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Compressive Sensing

In: Handbook of Mathematical Methods in Imaging

Author

Listed:
  • Massimo Fornasier

    (Faculty of Mathematics, Technische Universität München)

  • Holger Rauhut

    (Lehrstuhl C für Mathematik, RWTH Aachen University)

Abstract

Compressive sensing is a recent type of sampling theory, which predicts that sparse signals and images can be reconstructed from what was previously believed to be incomplete information. As a main feature, efficient algorithms such as ℓ 1-minimization can be used for recovery. The theory has many potential applications in signal processing and imaging. This chapter gives an introduction and overview on both theoretical and numerical aspects of compressive sensing.

Suggested Citation

  • Massimo Fornasier & Holger Rauhut, 2015. "Compressive Sensing," Springer Books, in: Otmar Scherzer (ed.), Handbook of Mathematical Methods in Imaging, edition 2, pages 205-256, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4939-0790-8_6
    DOI: 10.1007/978-1-4939-0790-8_6
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