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

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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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Yasuda, M., Watanabe, J., Kataoka, S., & Tanaka, K. (2018). Linear-time algorithm in Bayesian image denoising based on gaussian markov random field. IEICE Transactions on Information and Systems, E101D(6), 1629–1639. https://doi.org/10.1587/transinf.2017EDP7346

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