Information optimal compressive imaging: Design and implementation

Amit Ashok, James Huang, Yuzhang Lin, Ronan Kerviche

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Compressive imaging exploits sparsity/compressibility of natural scenes to reduce the detector count/read-out bandwidth in a focal plane array by effectively implementing compression during the acquisition process. How-ever, realizing the full potential of compressive imaging entails several practical challenges, such as measurement design, measurement quantization, rate allocation, non-idealities inherent in hardware implementation, scalable imager architecture, system calibration and tractable image formation algorithms. We describe an information-theoretic approach for compressive measurement design that incorporates available prior knowledge about natural scenes for more efficient projection design relative to random projections. Compressive measurement quantization and rate-allocation problem are also considered and simulation studies demonstrate the performance of random and information-optimal projection designs for quantized compressive measurements. Finally we demonstrate the feasibility of optical compressive imaging with a scalable compressive imaging hardware implementation that addresses system calibration and real-time image formation challenges. The experimental results highlight the practical effectiveness of compressive imaging with system design constraints, non-ideal system components and realistic system calibration.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
ISBN (Print)9781628412130
StatePublished - 2014
Event50 Years of Optical Sciences at the University of Arizona - San Diego, United States
Duration: Aug 19 2014Aug 20 2014


Other50 Years of Optical Sciences at the University of Arizona
Country/TerritoryUnited States
CitySan Diego


  • Compressive sensing
  • image formation
  • image priors
  • imaging
  • information theory
  • sparsity

ASJC Scopus subject areas

  • Applied Mathematics
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics


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