关键词: 人脸图像, 超分辨率, 马尔可夫网络, 非线性搜索

Abstract: This paper researches single face image super resolution algorithms based on learned image examples.Patch-based Markov network is used to express the mechanism of super-resolution processing.After dividing the high-resolution images and the corresponding low-resolution ones into patches，the training dataset is set up.Considering the requirements of Markov network computing and the difference among the images in training dataset，a patch position constraint operation is proposed for searching the matched patch and a nonlinear searching algorithm.These techniques can decrease the complexity of the searching operation and increase the effect of matching.After collecting the matched high-resolution patches，the proposed method directly uses them to integrate an output image.Experimental results demonstrate that the algorithm has a better performance and higher efficiency.

Key words: face image, super resolution, Markov network, nonlinear search