Compressive Sensing

Massimo Fornasier and Holger Rauhut

Chapter 6 in Handbook of Mathematical Methods in Imaging, 2011, pp 187-228 from Springer

Abstract: Abstract Compressive sensing is a new 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: Compressive Sense; Measurement Matrix; Restricted Isometry Property; Homotopy Method; Sparse Vector (search for similar items in EconPapers)
Date: 2011
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-0-387-92920-0_6

Ordering information: This item can be ordered from
http://www.springer.com/9780387929200

DOI: 10.1007/978-0-387-92920-0_6

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().