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Compressed sensing

Known as: Compressive sampling, Compressive sensing, Sparse recovery 
Compressed sensing (also known as compressive sensing, compressive sampling, or sparse sampling) is a signal processing technique for efficiently… 
Wikipedia

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2015
Review
2015
The steady growing number of quantum bits used in modern quantum information experiments gives rise to new problems. Especially… 
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Highly Cited
2014
Highly Cited
2014
  • D. Donoho
  • Computer Vision, A Reference Guide
  • 2014
  • Corpus ID: 30603419
Suppose x is an unknown vector in Ropfm (a digital image or signal); we plan to measure n general linear functionals of x and… 
Highly Cited
2012
Highly Cited
2012
Machine generated contents note: 1. Introduction to compressed sensing Mark A. Davenport, Marco F. Duarte, Yonina C. Eldar and… 
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Review
2011
Review
2011
Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous… 
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Highly Cited
2009
Highly Cited
2009
A stylized compressed sensing radar is proposed in which the time-frequency plane is discretized into an N times N grid. Assuming… 
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Highly Cited
2008
Highly Cited
2008
Compressed sensing is a technique to sample compressible signals below the Nyquist rate, whilst still allowing near optimal… 
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Review
2008
Review
2008
This article reviews the requirements for successful compressed sensing (CS), describes their natural fit to MRI, and gives… 
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Review
2008
Review
2008
This paper overviews the recent work on compressive sensing, a new approach to data acquisition in which analog signals are… 
Highly Cited
2007
Highly Cited
2007
This lecture note presents a new method to capture and represent compressible signals at a rate significantly below the Nyquist… 
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Highly Cited
2006
Highly Cited
2006
  • D. Donoho
  • IEEE Transactions on Information Theory
  • 2006
  • Corpus ID: 206737254
Suppose x is an unknown vector in Ropfm (a digital image or signal); we plan to measure n general linear functionals of x and… 
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