# Super-resolution of Omnidirectional Images Using Adversarial Learning

## TL;DR

This paper presents a novel adversarial learning approach for super-resolution of omnidirectional images, addressing artifacts in spherical space and introducing a specialized loss function to improve quality.

## Contribution

It introduces a spherical-content specific loss and a fast PatchGAN discriminator for enhanced ODI super-resolution, with a new dataset for training and evaluation.

## Key findings

- Improved super-resolution quality demonstrated on ODI dataset
- Effective handling of artifacts in spherical observational space
- Identification of new challenges for future ODI super-resolution research

## Abstract

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.