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Bayesian Methods for Image Super-resolution
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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 unfavorable 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.
BibTex reference:
@Article{Pickup07a,
author = "Lyndsey C. Pickup and David P. Capel and Stephen~J. Roberts and Andrew Zisserman",
title = "Bayesian Methods for Image Super-resolution",
journal = "The Computer Journal",
year = "2007",
}