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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

https://doi.org/10.1155/2007/23565

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

Abstract

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.

Keywords

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

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Authors’ Affiliations

(1)
Information Engineering Building, Department of Engineering Science, Parks Road, Oxford, OX1 3PJ, UK

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