An Efficient Gaussian Filter Based on Gaussian Symmetric Markov Random Field

Abstract

This article presents a new image denoising algorithm that uses Gaussian Symmetric Markov random fields based on maximum a posteriori estimation. First, an image denoising model based on Gaussian Symmetric Markov random fields is built, and the image denoising problem was converted to a maximum a posteriori estimation problem. The prior probability of an image can be estimated using the Gibbs distribution, which is equivalent to Markov random fields. Second, the maximum a posteriori estimation is calculated using the expectation-maximization algorithm and conjugate gradient method, where the expectation-maximization algorithm is used to estimate Gaussian Symmetric Markov random field hyper-parameters and the conjugate gradient method is used to calculate the criterion function. The experimental results for the synthetic images and the standard Berkeley segmentation datasets demonstrate the success of the proposed Gaussian denoising filter, as compared with the state-of-the-art methods such as BM3D, WNNM, SGWD-HMMs, SSLBD, DnCNN and BUIFD.