In this paper we present a method for single image super-resolution that use discrete cosine transform (DCT) interpolation and a sparse learning-based super-resolution method. The input low-resolution (LR) image is interpolated using both DCT interpolation and bicubic interpolation methods. The bicubic Interpolated image undergoes a process sparse coding using OMP algorithm. The obtained sparse coefficients are multiplied with high-resolution dictionary generated in the training phase, resulting in the intermediate high-resolution (HR) image. The final HR image is obtained by adding the DCT interpolated image and intermediate HR image. The experimental results demonstrate the effectiveness of the method proposed in terms of PSNR, SSIM and visual quality.