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

Muneki Yasuda; Junpei Watanabe; Shun Kataoka; Kazuyuki Tanaka

doi:10.1587/transinf.2017EDP7346

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

Muneki Yasuda, Junpei Watanabe, Shun Kataoka, Kazuyuki Tanaka

INF - Applied Information Sciences

Research output: Contribution to journal › Article › peer-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 language	English
Pages (from-to)	1629-1639
Number of pages	11
Journal	IEICE Transactions on Information and Systems
Volume	E101D
Issue number	6
DOIs	https://doi.org/10.1587/transinf.2017EDP7346
Publication status	Published - 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

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title = "Linear-time algorithm in Bayesian image denoising based on gaussian markov random field",

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.",

keywords = "Bayesian image denoising, EM algorithm, Gaussian Markov random field, Linear-time algorithm, Mean-field method",

author = "Muneki Yasuda and Junpei Watanabe and Shun Kataoka and Kazuyuki Tanaka",

note = "Funding Information: This work was partially supported by JST CREST Grant Number JPMJCR1402 and by JSPS KAKENHI Grant Numbers 15K00330, 15H03699, and 15K20870.",

year = "2018",

month = jun,

doi = "10.1587/transinf.2017EDP7346",

language = "English",

volume = "E101D",

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journal = "IEICE Transactions on Information and Systems",

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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.

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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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