Compressive sampling strategies for integrated microspectrometers

David J. Brady, Michael E. Gehm, Nikos Pitsianis, Xiaobai Sun

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

12 Scopus citations


We consider compressive sensing in the context of optical spectroscopy. With compressive sensing, the ratio between the number of measurements and the number of estimated values is less than one, without compromising the fidelity in estimation. A compressive sensing system is composed of a measurement subsystem that maps a signal to digital data and an inference algorithm that maps the data to a signal estimate. The inference algorithm exploits both the information captured in the measurement and certain a priori information about the signals of interest, while the measurement subsystem provides complementary, signal-specific information at the lowest sampling rate possible. Codesign of the measurement strategies, the model of a priori information, and the inference algorithm is the central problem of system design. This paper describes measurement constraints specific to optical spectrometers, inference models based on physical or statistical characteristics of the signals, as well as linear and nonlinear reconstruction algorithms. We compare the fidelity of sampling and inference strategies over a family of spectral signals.

Original languageEnglish (US)
Title of host publicationIntelligent Integrated Microsystems
StatePublished - 2006
Externally publishedYes
EventIntelligent Integrated Microsystems - Kissimmee, FL, United States
Duration: Apr 19 2006Apr 21 2006

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherIntelligent Integrated Microsystems
Country/TerritoryUnited States
CityKissimmee, FL


  • Adaptive inference
  • Compressive sensing
  • Multiplex spectroscopy
  • Spectroscopy

ASJC Scopus subject areas

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


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