  Hallucinating high frequency image details in single image super-resolution
is a challenging task. Traditional super-resolution methods tend to produce
oversmoothed output images due to the ambiguity in mapping between low and high
resolution patches. We build on recent success in deep learning based texture
synthesis and show that this rich feature space can facilitate successful
transfer and synthesis of high frequency image details to improve the visual
quality of super-resolution results on a wide variety of natural textures and
images.
