Minimum reconstruction error in feature-specific imaging

Jun Ke, Michael D. Stenner, Mark A. Neifeld

Research output: Contribution to journalConference articlepeer-review

7 Scopus citations


We describe theoretical and experimental results for a new class of optimal features for feature-specific imaging (FSI). In this paper, we theoretically solve the reconstruction problem without noise, and find a more general solution than principle component analysis (PCA). We present a generalized framework to Qnd FSI projection matrices. Using Stochastic Tunneling, we find an optimal solution in the presence of noise and under an energy conservation constraint. We also show that a non-negativity requirement does not significantly reduce system performance. Finally, we propose an experimental system for FSI using a polarization-based optical pipeline processor.

Original languageEnglish (US)
Article number02
Pages (from-to)7-12
Number of pages6
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2005
EventVisual Information Processing XIV - Orlando, FL, United States
Duration: Mar 29 2005Mar 30 2005


  • Feature-Specific Imaging
  • Image reconstruction
  • PCA
  • Stochastic Tunneling
  • Weiner operator

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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