  Traditionally, the main focus of image super-resolution techniques is on
recovering the most likely high-quality images from low-quality images, using a
one-to-one low- to high-resolution mapping. Proceeding that way, we ignore the
fact that there are generally many valid versions of high-resolution images
that map to a given low-resolution image. We are tackling in this work the
problem of obtaining different high-resolution versions from the same
low-resolution image using Generative Adversarial Models. Our learning approach
makes use of high frequencies available in the training high-resolution images
for preserving and exploring in an unsupervised manner the structural
information available within these images. Experimental results on the CelebA
dataset confirm the effectiveness of the proposed method, which allows the
generation of both realistic and diverse high-resolution images from
low-resolution images.
