Posterior Mean Super-Resolution with a Compound Gaussian Markov Random Field Prior

Details Posterior Mean Super-Resolution with a Compound Gaussian Markov Random Field Prior Posterior Mean Super-Resolution with a Compound Gaussian Markov Random Field Prior

2012-03-04

arxiv.org/abs/1203.0781v3

Computer Science

Computer Vision and Pattern Recognition

5 pages, 20 figures, 1 tables, accepted to ICASSP2012 (corrected 2012/3/23)

Scientific paper

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.

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