Image super-resolution algorithm based on SVM pre-classified learning
基于SVM预分类学习的图像超分辨率重建算法

Example-based super-resolution algorithm needed to run though the sample library with a high computing complexity. This method resulted in high calculation load and image degradation because of mis-matching. To resolve such problems, this paper proposed an algorithm based on SVM pre-classified learning. Before searching, it selected the subset of sample library similar to the color feature of object image, so as to ensure the content relevance between the sample patch and the input low-resolution image. In addition, the algorithm reduced the mis-matching greatly. The experimental results show the proposed algorithm has a better reconstruction performance than the example-based algorithm, which improves the program running speed in the precondition of accuracy.