This study proposes a super-resolution reconstruction algorithm with hierarchical clustering dictionary training and similar constraints for astronomical images, according to the theory of centralized sparse representation based image super-resolution reconstruction, thereby solving the problem of low imaging resolution of the astronomical images. In the dictionary training phase, a novel hierarchical clustering algorithm is used for classifying the sample image patches. Further, each image patch is independently trained to obtain multiple compact dictionaries. In the image reconstruction stage, the accuracy of the sparse coding coefficients is improved by suppressing the sparse coding noise. Subsequently, the sparse coefficients of the reconstructed image can be reasonably estimated based on the non-local self-similarity of the image. In addition, the image reconstruction process is globally constrained by the construction of non-local self-similar regularization terms. The experimental results denote that the proposed algorithm can effectively improve the resolution of astronomical images. Furthermore, the subjective visual effects and objective evaluation indicators of the reconstructed images are observed to be superior to those obtained by using other traditional super-resolution reconstruction algorithms.