Compressive sensing

Massimo Fornasier; Holger Rauhut

doi:10.1007/978-1-4939-0790-8_6

Compressive sensing

Massimo Fornasier, Holger Rauhut

Chair of Applied Numerical Analysis

Aachen University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

33 Scopus citations

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 `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.

Original language	English
Title of host publication	Handbook of Mathematical Methods in Imaging
Subtitle of host publication	Volume 1, Second Edition
Publisher	Springer New York
Pages	205-256
Number of pages	52
ISBN (Electronic)	9781493907908
ISBN (Print)	9781493907892
DOIs	https://doi.org/10.1007/978-1-4939-0790-8_6
State	Published - 1 Jan 2015

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Compressive sensing

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