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Recently, with the development of visual communication and image processing, there is a high demand for high-resolution images such as video surveillance, medical imaging, and so on. Therefore, the super-resolution technology that produces a high-resolution image from a set of shifted, blurred, and decimated versions is actively researched. However, most previously published techniques perform well only for small magnifications but get worse either in computational complexity or ringing artifacts for large magnifications. In this paper, we propose a hierarchical algorithm for high-magnification super-resolution image reconstruction. The proposed algorithm magnifies the low-resolution image in multiple steps. Experiment results show that the new approach is more efficient and can provide much better reconstruction quality in comparison with the reconstruction result in one step.