The STOne Transform: Multi-Resolution Image Enhancement and Compressive Video

Tom Goldstein, Lina Xu, Kevin F. Kelly, Richard Baraniuk

Research output: Contribution to journalArticle

28 Scopus citations

Abstract

Compressive sensing enables the reconstruction of high-resolution signals from under-sampled data. While the compressive methods simplify data acquisition, they require the solution of difficult recovery problems to make use of the resulting measurements. This paper presents a new sensing framework that combines the advantages of both the conventional and the compressive sensing. Using the proposed sum-to-one transform, the measurements can be reconstructed instantly at the Nyquist rates at any power-of-two resolution. The same data can then be enhanced to higher resolutions using the compressive methods that leverage sparsity to beat the Nyquist limit. The availability of a fast direct reconstruction enables the compressive measurements to be processed on small embedded devices. We demonstrate this by constructing a real-time compressive video camera.

Original languageEnglish (US)
Article number7229297
Pages (from-to)5581-5593
Number of pages13
JournalIEEE Transactions on Image Processing
Volume24
Issue number12
DOIs
StatePublished - Dec 1 2015

Keywords

  • Cameras
  • Image coding
  • Image reconstruction
  • Image resolution
  • Sensors
  • Streaming media
  • Transforms

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Software

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