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Open Access Publications from the University of California

Silhouette estimation.

  • Author(s): Paxman, Richard G
  • Carrara, David A
  • Walker, Paul D
  • Davidenko, Nicolas
  • et al.
Abstract

Silhouettes arise in a variety of imaging scenarios. Pristine silhouettes are often degraded via blurring, detector sampling, and detector noise. We present a maximum a posteriori estimator for the restoration of parameterized facial silhouettes. Extreme dealiasing and dramatic superresolution, well beyond the diffraction limit, are demonstrated through the use of strong prior knowledge.

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