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Handbook of Mathematical Methods in Imaging pp 205–256Cite as

Compressive Sensing

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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.

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Authors and Affiliations

  1. Faculty of Mathematics, Technische Universität München, Boltzmannstrasse 3, Garching, Germany

    Massimo Fornasier

  2. Lehrstuhl C für Mathematik, RWTH Aachen University, Templergraben 55, 52062, Aachen, Germany

    Holger Rauhut

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  1. Massimo Fornasier
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  2. Holger Rauhut
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Correspondence to Massimo Fornasier .

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  1. Computational Science Center, University of Vienna, Vienna, Austria

    Otmar Scherzer

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Fornasier, M., Rauhut, H. (2015). Compressive Sensing. In: Scherzer, O. (eds) Handbook of Mathematical Methods in Imaging. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0790-8_6

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