学习高阶马尔可夫随机场：评分匹配方法

传统的马尔可夫随机场模型有两个内在的缺陷：邻域的低阶性和参数的手动选择。提出一种新的机器学习方法——评分匹配法，从训练图像数据中学习得到一组高阶马尔可夫随机场模型参数。为了验证通过学习得到的马尔可夫随机场模型的能力，将学习得到的参数向量通过贝叶斯规则应用于图像去噪。实验结果表明：不管是根据峰值信噪比的大小还是根据主观视觉，都能取得优秀的去噪效果，从而表明该学习方法的有效性。

Scoring matching approach:Learning high order Markov random fields

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.