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.

References in corpus (2)

• Gaussian Belief Propagation Solver for Systems of Linear Equations
• Steerable Discrete Cosine Transform

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