Markov network-based nonlinear algorithm of face image super-resolution
基于马尔可夫网络人脸图像超分辨率非线性算法*

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