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  • Research Article
  • Open Access

Overcoming Registration Uncertainty in Image Super-Resolution: Maximize or Marginalize?

  • 1Email author,
  • 1,
  • 1 and
  • 1
EURASIP Journal on Advances in Signal Processing20072007:023565

  • Received: 15 September 2006
  • Accepted: 4 May 2007
  • Published:


In multiple-image super-resolution, a high-resolution image is estimated from a number of lower-resolution images. This usually involves computing the parameters of a generative imaging model (such as geometric and photometric registration, and blur) and obtaining a MAP estimate by minimizing a cost function including an appropriate prior. Two alternative approaches are examined. First, both registrations and the super-resolution image are found simultaneously using a joint MAP optimization. Second, we perform Bayesian integration over the unknown image registration parameters, deriving a cost function whose only variables of interest are the pixel values of the super-resolution image. We also introduce a scheme to learn the parameters of the image prior as part of the super-resolution algorithm. We show examples on a number of real sequences including multiple stills, digital video, and DVDs of movies.


  • Cost Function
  • Image Registration
  • Digital Video
  • Imaging Model
  • Generative Imaging

Authors’ Affiliations

Information Engineering Building, Department of Engineering Science, Parks Road, Oxford, OX1 3PJ, UK


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© Lyndsey C. Pickup et al.s 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.