Practical compressive sensing of large images

Yair Rivenson, Adrian Stern

Research output: Contribution to conferencePaperpeer-review

29 Scopus citations


Compressive imaging (CI) is a natural branch of compressed sensing (CS). One of the main difficulties in implementing CI is that, unlike many other CS applications, it involves huge amount of data. This data load has extensive implications for the complexity of the optical design, for the complexity of calibration, for data storage requirements. As a result, practical CI implementations are mostly limited to relative small image sizes. Recently we have shown that it is possible to overcome these problems by using a separable imaging operator. We have demonstrated that separable imaging operator permits CI of megapixel size images and we derived a theoretical bound for oversampling factor requirements. Here we further elaborate the tradeoff of using separable imaging operator, present and discuss additional experimental results.

Original languageEnglish GB
StatePublished - 20 Nov 2009
EventDSP 2009:16th International Conference on Digital Signal Processing - Santorini, Greece
Duration: 5 Jul 20097 Jul 2009


ConferenceDSP 2009:16th International Conference on Digital Signal Processing


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

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing


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