Can compressed sensing beat the Nyquist sampling rate?

Leonid P. Yaroslavsky*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

7 Scopus citations


The data saving capability of "compressed sensing (sampling)" in signal discretization is disputed and found to be far below the theoretical upper bound defined by the signal sparsity. It is demonstrated on a simple and intuitive example, that, in a realistic scenario for signals that are believed to be sparse, one can achieve a substantially larger saving than compressing sensing can. It is also shown that frequent assertions in the literature that "compressed sensing" can beat the Nyquist sampling approach are misleading substitutions of terms and are rooted in misinterpretation of the sampling theory.

Original languageEnglish
Article number150246C
JournalOptical Engineering
Issue number7
StatePublished - 1 Jul 2015


  • compressed sensing
  • sampling
  • sampling theorem


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