Linear-Time Algorithm in Bayesian Image Denoising based on Gaussian Markov Random Field
This work addresses computational bottlenecks in image denoising for applications requiring fast processing, though it is incremental as it builds on existing GMRF models.
The paper tackles Bayesian image denoising using a Gaussian Markov random field model by proposing a new algorithm that achieves O(n)-time complexity, including hyperparameter estimation, making it state-of-the-art in computational efficiency, with numerical experiments confirming its practical effectiveness.
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.