  An omnidirectional image (ODI) enables viewers to look in every direction
from a fixed point through a head-mounted display providing an immersive
experience compared to that of a standard image. Designing immersive virtual
reality systems with ODIs is challenging as they require high resolution
content. In this paper, we study super-resolution for ODIs and propose an
improved generative adversarial network based model which is optimized to
handle the artifacts obtained in the spherical observational space.
Specifically, we propose to use a fast PatchGAN discriminator, as it needs
fewer parameters and improves the super-resolution at a fine scale. We also
explore the generative models with adversarial learning by introducing a
spherical-content specific loss function, called 360-SS. To train and test the
performance of our proposed model we prepare a dataset of 4500 ODIs. Our
results demonstrate the efficacy of the proposed method and identify new
challenges in ODI super-resolution for future investigations.
