Overcoming registration uncertainty in image super-resolution: maximize or marginalize?

  • Authors:

  • Lyndsey C. Pickup;David P. Capel;Stephen J. Roberts;Andrew Zisserman

  • Affiliations:

  • Information Engineering Building, Department of Engineering Science, Oxford, UK;Information Engineering Building, Department of Engineering Science, Oxford, UK;Information Engineering Building, Department of Engineering Science, Oxford, UK;Information Engineering Building, Department of Engineering Science, Oxford, UK

  • Venue:
  • EURASIP Journal on Advances in Signal Processing
  • Year:
  • 2007

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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.