Posterior Mean Super-Resolution with a Compound Gaussian Markov Random Field Prior
This work addresses image super-resolution for applications like photography or surveillance, but it appears incremental as it builds on prior methods with modest improvements.
The authors tackled the problem of super-resolution by proposing a posterior mean estimator with a compound Gaussian Markov random field prior, which roughly outperformed existing methods in experiments.
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.