Local retrodiction models for photon-noise-limited images

Matthias Sonnleitner, John Jeffers, Stephen M. Barnett

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

1 Citation (Scopus)
24 Downloads (Pure)


Imaging technologies working at very low light levels acquire data by attempting to count the number of photons impinging on each pixel. Especially in cases with, on average, less than one photocount per pixel the resulting images are heavily corrupted by Poissonian noise and a host of successful algorithms trying to reconstruct the original image from this noisy data have been developed. Here we review a recently proposed scheme that complements these algorithms by calculating the full probability distribution for the local intensity distribution behind the noisy photocount measurements. Such a probabilistic treatment opens the way to hypothesis testing and confidence levels for conclusions drawn from image analysis.

Original languageEnglish
Title of host publicationOptics, Photonics and Digital Technologies for Imaging Applications IV
EditorsPeter Schelkens, Touradj Ebrahimi, Gabriel Cristóbal, Frédéric Truchetet, Pasi Saarikko
Place of PublicationWashington
Publication statusPublished - 29 Apr 2016
EventOptics, Photonics and Digital Technologies for Imaging Applications IV - Brussels, Belgium
Duration: 5 Apr 20166 Apr 2016

Publication series

NameProceedings of SPIE
PublisherSociety of Photo-Optical Instrumentation Engineers
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceOptics, Photonics and Digital Technologies for Imaging Applications IV


  • Bayesian inference
  • image analysis
  • image optimisation
  • image retrodiction
  • photon-limited imaging
  • Poisson noise

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