1. INTRODUCTION
Image super-resolution (SR) aims to generate the latent high-resolution (HR) images from low-resolution (LR) ones by generating information that does not exist in the LR images [1], [2]. Single-image SR (SISR) by learning from examples dates back to the seminal works of Freeman [3], [4], which implicitly learned features from a database of HR and LR image patch pairs. These example natural image patch pairs were employed as data-driven priors to extrapolate high frequency image details. Example-based SISR has later been applied to target specific classes of images, e.g., face images [5], [6]. These class-based SISR algorithms have yielded superior results, because they were able to better capture class-specific image priors.