Image super-resolution aims to recover a visually pleasing high resolution image from one or multiple low resolution images. It plays an essential role in a variety of real-world applications. In this paper, we propose a novel hybrid example-based single image super-resolution approach which integrates learning from both external and internal exemplars. Given an input image, a proxy image with the same resolution as the target high-resolution image is first generated from a set of externally-learnt regression models. We then perform a coarse-to-fine gradient-level self-refinement on the proxy image guided by the input image. Finally, the refined high-resolution gradients are fed into a uniform energy function to recover the final output. Extensive experiments demonstrate that our framework outperforms the recent state-of-the-art single image super-resolution approaches both quantitatively and qualitatively.