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.
@article{arxiv.1203.0781,
title = {Posterior Mean Super-Resolution with a Compound Gaussian Markov Random Field Prior},
author = {Takayuki Katsuki and Masato Inoue},
journal= {arXiv preprint arXiv:1203.0781},
year = {2023}
}