Linear estimation theory applied to the evaluation of a priori information and system optimization in coded-aperture imaging

Warren E. Smith, Harrison H. Barrett

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Linear estimation theory is developed in the context of object reconstruction from data obtained by a general shiftvariant imaging system. The formalism adopts nonstationary first- and second-order statistics of the object and noise classes as a priori information. In addition, a metric for system optimization that depends on the a priori information is presented. The role of this a priori information as derived from several different training sets is then studied with respect to reconstruction performance for various noise levels in the data, using a tomographic codedaperture system as the model. In a separate experiment, a simple coded-aperture system is optimized to a particular object class, and the results are compared with those from an earlier optimization experiment.

Original languageEnglish (US)
Pages (from-to)315-330
Number of pages16
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Volume5
Issue number3
DOIs
StatePublished - Mar 1988

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition

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