Variational Bayesian inference for binary image restoration using Ising model
Moonsoo Jang, Younshik Chung · Communications for Statistical Applications and Methods · 2022
In this paper, the focus on the removal noise in the binary image based on the variational Bayesian method with the Ising model.The observation and the latent variable are the degraded image and the original image, respectively.The posterior distribution is built using the Markov random field and the Ising model.Estimating the posterior distribution is the same as reconstructing a degraded image.MCMC and variational Bayesian inference are two methods for estimating the posterior distribution.However, for the sake of computing efficiency, we adapt the variational technique.When the image is restored, the iterative method is used to solve the recursive problem.Since there are three model parameters in this paper, restoration is implemented using the VECM algorithm to find appropriate parameters in the current state.Finally, the restoration results are shown which have maximum peak signal-to-noise ratio (PSNR) and evidence lower bound (ELBO).