%0 Journal Article
%T Scoring matching approach:Learning high order Markov random fields<br>学习高阶马尔可夫随机场:评分匹配方法
%A LU Xiao-lei
%A WANG Fu-rong
%A HUANG Ben-xiong
%A <br>鲁晓磊
%A 王芙蓉
%A 黄本雄
%J 计算机应用
%D 2008
%I 
%X Traditional Markov Random Field (MRF) models have two inherent limitations that are low order property of pixel neighborhoods and selecting parameters by hand. In this paper, we adopted a new machine learning method of score matching and get a group of parameters of high order MRF models by learning from training image data. We demonstrated the capabilities of the learning MRF models by applying them to image denoising according to Bayesian rule. Imaging denoising experiments show that our denoising algorithm can produce excellent result in the Peak Signal-to-Noise Ratios (PSNR) and subjective visual effect. Thus, our learning method is effective.
%K high order Markov random field
%K score matching
%K image denoising<br>高阶马尔可夫随机场
%K 评分匹配
%K 图像去噪
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=0B6CE9775B0D181A60953DC4DDB6DC3D&yid=67289AFF6305E306&vid=D3E34374A0D77D7F&iid=F3090AE9B60B7ED1&sid=20481FD9F9546A61&eid=1E97FD20C6EBC45D&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=18