In this paper, we consider Bayesian image denoising based on a GaussianMarkov random field (GMRF) model, for which we propose an new algorithm. Ourmethod can solve Bayesian image denoising problems, including hyperparameterestimation, 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 astate-of-the-art algorithm for the present problem setting. Moreover, theresults of our numerical experiments we show our method is in fact effective inpractice.
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机译：在本文中，我们考虑基于高斯马尔可夫随机场（GMRF）模型的贝叶斯图像去噪，为此我们提出了一种新算法。我们的方法可以在$ O（n）$-time中解决贝叶斯图像降噪问题，包括超参数估计，其中$ n $是给定图像中的像素数。从计算时间的顺序来看，这是一种状态-当前问题设置的最新算法。此外，我们的数值实验结果表明，我们的方法实际上是有效的实践。
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