Improved image super-resolution by Support Vector Regression

Support Vector Machine (SVM) can construct a hyperplane in a high or infinite dimensional space which can be used for classification. Its regression version, Support Vector Regression (SVR) has been used in various image processing tasks. In this paper, we develop an image super-resolution algorithm based on SVR. Experiments demonstrated that our proposed method with limited training samples outperforms some of the state-of-the-art approaches and during the super-resolution process the model learned by SVR is robust to reconstruct edges and fine details in various testing images.