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

TL;DR

This paper introduces a super-resolution method using a posterior mean estimator with a compound Gaussian MRF prior, effectively preserving edges and improving image quality over existing techniques.

Contribution

It develops a novel super-resolution approach combining posterior mean estimation with a compound Gaussian MRF prior, optimized via variational Bayes with computational simplifications.

Findings

Method roughly outperforms existing super-resolution techniques

Preserves edges effectively in high-resolution images

Uses variational Bayes with Taylor approximations for efficiency

Abstract

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.