Abstract: The spatial resolution of the image is limited by many factors including the environment of the imaging,the manufacturing technology of the hardware and the cost.A codebook mapping based single image super resolution(SR) method via dictionary learning and structural self-similarity is proposed to promote the spatial resolution of the image.Firstly,the images in the training set are used to construct two codebooks corresponding to the low and the high frequency components of image patches,and the two codebooks are taken as the training samples to learn two dictionaries respectively.Then,the similar image patches of each input image patch in the initial reconstructed image are used to construct the nonlocal constraint.Finally,the sparse representation coefficient of the input image patch is obtained by solving an 0l norm minimization problem with the nonlocal constraint,and the high resolution image patch is then reconstructed by using the low and the high dictionaries.The input image patch is represented by the dictionaries,rather than the codebooks,therefore the computational efficiency is increasing.Similar image patches are used to construct the regularization constraint,consequently the quality of the reconstructed image is im-proved.Experimental results demonstrate that the proposed method achieves better reconstructed results compared with LLE,Sc SR and NARM methods.