Deterministic algorithm for nonlinear markov random field model

Yoshinor Ohno, Kenj Nagata, Tats Kuwatani, Hayar Shouno, Masat Okada

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


We propose a deterministic algorithm for image restoration using a nonlinear Markov random field (MRF) model. Recent advances in measurement techniques allow us to obtain a large quantity of imaging data in various natural science fields. These data are often exposed to observation noise. For the removal of noise from imaging data, we use an MRF model, in which the Bayesian inference framework enables us to estimate hyperparameters through freeenergy minimization. When a nonlinear function represents an observation process, a Markov chain Monte Carlo (MCMC) method is often used for image restoration. An MCMC method retains nonlinearity, but it is a probabilistic algorithm, which increases computational cost. The proposed deterministic algorithm linearizes the observation process to achieve more efficient hyperparameter estimation and image restoration. We also applied the proposed algorithm to artificial images to show its efficiency.

Original languageEnglish
Article number064006
Journaljournal of the physical society of japan
Issue number6
Publication statusPublished - 2012 Jun


  • Deterministic algorithm
  • Free-energy
  • Hyperparameter estimation
  • Image restoration
  • Linear approximation
  • Nonlinear MRF model

ASJC Scopus subject areas

  • Physics and Astronomy(all)


Dive into the research topics of 'Deterministic algorithm for nonlinear markov random field model'. Together they form a unique fingerprint.

Cite this