Expected Patch Log Likelihood with a Prior of Mixture of Matrix Normal Distributions for Image Denoising
Xiuling Zhou, Xiaoqiao Zhang, Ping Guo · 2018
Mixture of Matrix Normal Distributions (MMND) is the two dimensional extension of Gaussian Mixture Model, which has been widely applied for clustering three-way data. It is the key issue to build image prior model for solving image denoising problem. In this paper, the Expected Patch Log Likelihood (EPLL) with a prior of MMND is proposed for image denoising. Expectation Maximization algorithm and flip-flop algorithm are adopted to estimate the parameters in MMND. Regularization parameter of covariance matrix is selected by the criterion of minimization the Kullback-Leibler information measure (KLIM) with a heuristic approximation. Under the framework of the EPLL, the approximate MAP estimation for the unknown image x is developed. It is shown by experiments that MMND based patch prior performs well on image denoising problem.