Super-resolution reconstruction plays an important role in adding the image details and improving the visual perception. In order to effectively exploit the effective information hidden in the image itself, we proposed a single image super-resolution reconstruction method based on self-similarity and sparse representation. The method combines sparse K-SVD dictionary learning and nonlocal means，which are used to add the effective information hidden in the same scale and across different scales structural self-similarity into the maximum a posteriori probability estimation framework by two different regularization terms. Then, a local optimal solution is obtained by using the gradient descent algorithm. The experimental results show that our method has a better improvement both visually and quantitatively.