Super resolution is a technique that obtains high-resolution (HR) images from the corresponding low-resolution (LR) ones and moreover, makes these HR images as natural as possible. There are usually some artifacts of these HR images caused by the lack of the information of the detailed textures (high-frequency information). In our research, an algorithm based on weighted vector quantization is proposed to predict the high-frequency information. First of all, the classified vector quantization is adopted to establish a pair of codebooks of high-frequency information for LR and HR patches. Second, three code vectors are selected from the LR codebooks based on the correlation coefficients to establish a multilinear regression model with the input LR patch and meanwhile the weights are decided adaptively. Then, three corresponding HR code vectors are used with the weights to reconstruct the high-frequency information of the HR patch. Finally, the reconstructed high-frequency information is used to refine the preliminarily upscaled HR image. The experimental results show that the performance of the proposed algorithm is good not only in objective but also in subjective measurement.