Deterministic algorithm for nonlinear markov random field model

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

研究成果: Article査読

4 被引用数 (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.

ジャーナルjournal of the physical society of japan
出版ステータスPublished - 2012 6

ASJC Scopus subject areas

  • 物理学および天文学(全般)


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