  This paper addresses the problem of super-resolution: constructing a highly
resolved (HR) image from a low resolved (LR) one. Recent unsupervised
approaches search the latent space of a StyleGAN pre-trained on HR images, for
the image that best downscales to the input LR image. However, they tend to
produce out-of-domain images and fail to accurately reconstruct HR images that
are far from the original domain. Our contribution is twofold. Firstly, we
introduce a new regularizer to constrain the search in the latent space,
ensuring that the inverted code lies in the original image manifold. Secondly,
we further enhanced the reconstruction through expanding the image prior around
the optimal latent code. Our results show that the proposed approach recovers
realistic high-quality images for large magnification factors. Furthermore, for
low magnification factors, it can still reconstruct details that the generator
could not have produced otherwise. Altogether, our approach achieves a good
trade-off between fidelity and realism for the super-resolution task.
