The goal of single-image super-resolution is to generate a high-quality high-resolution image based on a given low-resolution input. It is an ill-posed problem which requires exemplars or priors to better reconstruct the missing high-resolution image details. In this paper, we propose to split the feature space into numerous subspaces and collect exemplars to learn priors for each subspace, thereby creating effective mapping functions. The use of split input space facilitates both feasibility of using simple functions for super-resolution, and efficiency of generating high-resolution results. High-quality high-resolution images are reconstructed based on the effective learned priors. Experimental results demonstrate that the proposed algorithm performs efficiently and effectively over state-of-the-art methods.