Abstract: Aiming at the problem of super-resolution reconstruction for single noised image,in terms of sparse representation of over-complete dictionary,a super-resolution model is proposed.The K-SVD algorithm is used directly for learning the dictionary for low-resolution images.The dictionary for high-resolution images is got by optimizing the approximating error of the isomorphic sparse representation coefficients,which are got by learning the dictionary for low-resolution images.The representation coefficients are multiplied by the high-resolution dictionary to get the approximative high-resolution image patches.To make the reconstructed image robust to noise,the denoising method via sparse representation is used to get the final image from the overlapped approximative high-resolution image patches.The experimental results show that the proposed model obtains better outcome both in subjective visual effect and objective evaluation criteria,and demonstrates the effective of the model and algorithm.