Abstract: For moon rover navigation and exploration mission during the 2nd stage of Chang’e project, high-resolution images are necessary. So a moon rover image super-resolution reconstruction algorithm via using compressed sensing was presented. The target is to reconstruct an original image from its blurred and down-scaled noisy version. The algorithm assumed a local Sparse-Land model on image patches, serving as regularization. The images from Apollo Project, tests in the 2nd stage of Chang’e project and natural image database were used in extracting patches for building two dictionaries. The K-SVD algorithm was used in training the dictionaries. Then the effective segmentation was implemented on low-resolution image. Through solving optimization problem via 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 by using least squares algorithm. Numerical experiments about moon rover images from tests in the 2nd stage of Chang’e project demonstrated the effectiveness of the proposed algorithm. Moreover, the proposed algorithm outperforms bicubic interpolation based method and the algorithm via Yang in terms of visual quality and the peak signal to noise ratio.