TY - JOUR
T1 - Linear-time algorithm in Bayesian image denoising based on gaussian markov random field
AU - Yasuda, Muneki
AU - Watanabe, Junpei
AU - Kataoka, Shun
AU - Tanaka, Kazuyuki
N1 - Funding Information:
This work was partially supported by JST CREST Grant Number JPMJCR1402 and by JSPS KAKENHI Grant Numbers 15K00330, 15H03699, and 15K20870.
Publisher Copyright:
Copyright © 2018 The Institute of Electronics, Information and Communication Engineers.
PY - 2018/6
Y1 - 2018/6
N2 - 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.
AB - 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.
KW - Bayesian image denoising
KW - EM algorithm
KW - Gaussian Markov random field
KW - Linear-time algorithm
KW - Mean-field method
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U2 - 10.1587/transinf.2017EDP7346
DO - 10.1587/transinf.2017EDP7346
M3 - Article
AN - SCOPUS:85048012334
VL - E101D
SP - 1629
EP - 1639
JO - IEICE Transactions on Information and Systems
JF - IEICE Transactions on Information and Systems
SN - 0916-8532
IS - 6
ER -