Compressed imaging with a separable sensing operator

Yair Rivenson, Adrian Stern

Research output: Contribution to journalArticlepeer-review

137 Scopus citations


Compressive imaging (CI) is a natural branch of compressed sensing (CS). Although a number of CI implementations have started to appear, the design of efficient CI system still remains a challenging problem. One of the main difficulties in implementing CI is that it involves huge amounts of data, which has far-reaching implications for the complexity of the optical design, calibration, data storage and computational burden. In this paper, we solve these problems by using a two-dimensional separable sensing operator. By so doing, we reduce the complexity by factor of 106 for megapixel images. We show that applying this method requires only a reasonable amount of additional samples.

Original languageEnglish
Pages (from-to)449-452
Number of pages4
JournalIEEE Signal Processing Letters
Issue number6
StatePublished - 26 May 2009


  • Compressed sensing
  • Compressive imaging
  • Kronecker product
  • Mutual coherence
  • Separable operator

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


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