Bayesian Restoration of Digital Images Employing Markov Chain Monte Carlo a Review

TL;DR

This paper reviews Bayesian methods for digital image restoration using Monte Carlo techniques, covering probabilistic models, statistical estimation, and algorithms for image reconstruction.

Contribution

It provides a comprehensive overview of Bayesian image restoration techniques employing Monte Carlo methods, including models, algorithms, and statistical approaches.

Findings

Summarizes likelihood, prior, and posterior models for image restoration.

Discusses Markov Chain Monte Carlo and cluster algorithms.

Highlights statistical estimation techniques for true image recovery.

Abstract

A review of Bayesian restoration of digital images based on Monte Carlo techniques is presented. The topics covered include Likelihood, Prior and Posterior distributions, Poisson, Binay symmetric channel, and Gaussian channel models of Likelihood distribution,Ising and Potts spin models of Prior distribution, restoration of an image through Posterior maximization, statistical estimation of a true image from Posterior ensembles, Markov Chain Monte Carlo methods and cluster algorithms.