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The Computer Journal Advance Access originally published online on October 27, 2007
The Computer Journal 2009 52(1):101-113; doi:10.1093/comjnl/bxm091
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© The Author 2007. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Bayesian Methods for Image Super-Resolution

Lyndsey C. Pickup1,*, David P. Capel2, Stephen J. Roberts1 and Andrew Zisserman1

1 Information Engineering Building, Department of Engineering Science, Parks Road, Oxford OX1 3PJ, UK
2 2D3 Ltd

* Corresponding author: elle{at}robots.ox.ac.uk

Received 22 January 2007; revised 11 September 2007

We present a novel method of Bayesian image super-resolution in which marginalization is carried out over latent parameters such as geometric and photometric registration and the image point-spread function. Related Bayesian super-resolution approaches marginalize over the high-resolution image, necessitating the use of an unfavourable image prior, whereas our method allows for more realistic image prior distributions, and reduces the dimension of the integral considerably, removing the main computational bottleneck of algorithms such as Tipping and Bishop's Bayesian image super-resolution. We show results on real and synthetic datasets to illustrate the efficacy of our method.

Key Words: super-resolution • Bayesian • image prior • image registration


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