An Introduction To Compressive Sampling

by E.J. Candes, M.B. Wakin
IEEE Signal Processing Magazine ()


Conventional approaches to sampling signals or images follow Shannon's theorem: the sampling rate must be at least twice the maximum frequency present in the signal (Nyquist rate). In the field of data conversion, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon representation - the signal is uniformly sampled at or above the Nyquist rate. This article surveys the theory of compressive sampling, also known as compressed sensing or CS, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use.

Cite this document (BETA)

Readership Statistics

5309 Readers on Mendeley
by Discipline
by Academic Status
28% Ph.D. Student
20% Student (Master)
13% Student (Bachelor)
by Country
3% United States
1% United Kingdom
1% India

Sign up today - FREE

Mendeley saves you time finding and organizing research. Learn more

  • All your research in one place
  • Add and import papers easily
  • Access it anywhere, anytime

Start using Mendeley in seconds!

Sign up & Download

Already have an account? Sign in