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

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arXiv · 1710.07393

Linear-Time Algorithm in Bayesian Image Denoising based on Gaussian Markov Random Field

Muneki Yasuda·Junpei Watanabe·Shun Kataoka·kazuyuki Tanaka

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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.

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stat.ML cond-mat.dis-nn cs.CV

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BibTeX RIS

Muneki Yasuda, Junpei Watanabe, Shun Kataoka, kazuyuki Tanaka. 2017-10-20. Linear-Time Algorithm in Bayesian Image Denoising based on Gaussian Markov Random Field. https://doi.org/10.1587/transinf.2017edp7346

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