Some of the traditional single-frame super-resolution (SR) reconstruction algorithms can not get good reconstruction results, although they learns many different types of images. Therefore, a super-resolution method combined with the Support Vector Regression (SVR) and raster-scan actions was proposed. Firstly, image patches were extracted from a group of high resolution (HR) images and the corresponding low resolution (LR) edition by the raster-scan actions, and input vectors and pixel vectors were taken out from the patches. The Log algorithm was used to determine that those patches were belong to high-frequency space or low frequency space then to construct the high and low frequency vector pairs. Then, those optimized vector pairs were trained by the SVR and two dictionaries in high/low frequency spaces were built eventually. Furthermore, input vectors were extracted from tested LR images in high/low frequency space, and the SVR tool was used to predict the SR pixel labels and the predicted pixels were added to bicubic interpolation image based on LR edition. Finally, the SR image was obtained by post-processing the previous image. In comparison with other algorithms, experimental results indicate that the proposed method provides good visual effects. It enhances its Peak Signal-to-Noise Ration (PSNR) and Structural Similarity Index Measurement (SSIM) by 3.1%-5.3% and 1.5%-8.1% on different images, respectively as compared with bicubic interpolation method.