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Posterior Mean Super-Resolution with a Compound Gaussian Markov Random Field Prior
This manuscript proposes a posterior mean (PM) super-resolution (SR) method
with a compound Gaussian Markov random field (MRF) prior. SR is a technique to
estimate a spatially high-resolution image from observed multiple
low-resolution images. A compound Gaussian MRF model provides a preferable
prior for natural images that preserves edges. PM is the optimal estimator for
the objective function of peak signal-to-noise ratio (PSNR). This estimator is
numerically determined by using variational Bayes (VB). We then solve the
conjugate prior problem on VB and the exponential-order calculation cost
problem of a compound Gaussian MRF prior with simple Taylor approximations. In
experiments, the proposed method roughly overcomes existing methods.Comment: 5 pages, 20 figures, 1 tables, accepted to ICASSP2012 (corrected
2012/3/23
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