For Chang’e-3 landing mission in the 2nd stage of Chang’e project, high-resolution images were necessary. So a lunar satellite images super-resolution reconstruction algorithm via using compressed sensing was presented. The images from Apollo project, CE-1, CE-2 and tests in the 2nd stage of Chang’e project were used in extracting patches and the dictionaries Ah and Al were built with joint training. Through solving optimization problem via Regularized Orthogonal Matching Pursuit algorithm, the sparse representation for each low-resolution image patch with respect to Al was obtained, and the representation coefficients were applied to Ah in order to generate the corresponding high-resolution image patch. At the end of experiment, the high-resolution image which satisfied the reconstruction constraint was achieved. Numerical experiments demonstrated the effectiveness of the proposed algorithm. Moreover, the proposed algorithm outperforms traditional methods in terms of visual quality, the Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE).