Linear-time algorithm in Bayesian image denoising based on gaussian markov random field

Muneki Yasuda, Junpei Watanabe, Shun Kataoka, Kazuyuki Tanaka

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we consider Bayesian image denoising based on a Gaussian Markov random field (GMRF) model, for which we propose an new algorithm. Our method can solve Bayesian image denoising problems, including hyperparameter estimation, in O(n)-time, where n is the number of pixels in a given image. From the perspective of the order of the computational time, this is a state-of-the-art algorithm for the present problem setting. Moreover, the results of our numerical experiments we show our method is in fact effective in practice.

Original languageEnglish
Pages (from-to)1629-1639
Number of pages11
JournalIEICE Transactions on Information and Systems
VolumeE101D
Issue number6
DOIs
Publication statusPublished - 2018 Jun

Keywords

  • Bayesian image denoising
  • EM algorithm
  • Gaussian Markov random field
  • Linear-time algorithm
  • Mean-field method

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Linear-time algorithm in Bayesian image denoising based on gaussian markov random field'. Together they form a unique fingerprint.

Cite this