Compressive Sensing

Massimo Fornasier () and Holger Rauhut ()
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Massimo Fornasier: Faculty of Mathematics, Technische Universität München
Holger Rauhut: Lehrstuhl C für Mathematik, RWTH Aachen University

A chapter in Handbook of Mathematical Methods in Imaging, 2015, pp 205-256 from Springer

Abstract: 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.

Keywords: Compressed Sensing (CS); Iteratively Re-weighted Least Squares (IRLS); Restricted Isometry Property (RIP); Gelfand Widths; Phase Retrieval Problem (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-1-4939-0790-8_6

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