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In this paper, we propose a new algorithm to estimate a super-resolution image from a given low-resolution image, by adding high-frequency information that is extracted from natural high-resolution images in the training dataset. The selection of the high-frequency information from the training dataset is accomplished in two steps: a nearest-neighbor search algorithm is used to select the closest images from the training dataset, which can be implemented in the GPU, and a sparse-representation algorithm is used to estimate a weight parameter to combine the high-frequency information of selected images. This simple but very powerful super-resolution algorithm can produce state-of-the-art results. Qualitatively and quantitatively, we demonstrate that the proposed algorithm outperforms existing common practices.