Image super-resolution based on multikernel regression

Abstract

In this paper, a novel approach to single image super-resolution based on the multikernel regression is presented. This approach's core is 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 approach is promising, but the kernel selection is a critical problem. In order to avoid selecting the kernel via large amounts 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 super-resolution methods.