In this paper, a novel approach to single image super-resolution based on the multikernel regression is presented. This approach aims to learn the map between the space of high-resolution image patches and the space of blurred high-resolution image patches, which are the interpolation results generated from the corresponding low-resolution images. Kernel regression based super-resolution approaches are promising, but kernel selection is a critical problem. In order to avoid selecting kernels via a large number of cross-verifications, the multikernel regression is applied to learn the map function. This approach is efficient and the experimental results show that it manifests a high-quality performance in comparison with other superresolution methods.