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.
@article{arxiv.1710.07393,
title = {Linear-Time Algorithm in Bayesian Image Denoising based on Gaussian Markov Random Field},
author = {Muneki Yasuda and Junpei Watanabe and Shun Kataoka and kazuyuki Tanaka},
journal= {arXiv preprint arXiv:1710.07393},
year = {2020}
}