Information-optimal adaptive compressive imaging

Amit Ashok, James L. Huang, Mark A. Neifeld

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

1 Scopus citations

Abstract

We adopt a sequential Bayesian experiment design framework for compressive imaging wherein the measurement basis is data dependent and therefore adaptive. The criteria for measurement basis design employs the task-specific information (TSI), an information theoretic metric, that is conditioned on the past measurements. A Gaussian scale mixture prior model is used to represent compressible natural scenes in theWavelet basis. The resulting adaptive compressive imager design yields significant performance improvements compared to a static compressive imager using random projections.

Original languageEnglish (US)
Title of host publicationConference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
Pages1255-1259
Number of pages5
DOIs
StatePublished - Dec 1 2011
Event45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011 - Pacific Grove, CA, United States
Duration: Nov 6 2011Nov 9 2011

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
CountryUnited States
CityPacific Grove, CA
Period11/6/1111/9/11

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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    Ashok, A., Huang, J. L., & Neifeld, M. A. (2011). Information-optimal adaptive compressive imaging. In Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011 (pp. 1255-1259). [6190217] (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2011.6190217