Super-resolution of Omnidirectional Images Using Adversarial Learning

Abstract

An omnidirectional image (ODI) enables viewers to look in every direction from a fixed point through a headmounted 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.

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Citation

Paper accepted in IEEE 21st International Workshop on Multimedia Signal Processing (MMSP 2019) 
Please cite our paper in your publications if it helps your research:

@inproceedings{mmspsr2019,
title = {Super-resolution of Omnidirectional Images Using Adversarial Learning},
author = {Cagri Ozcinar and Aakanksha Rana and Aljosa Smolic},
year = {2019},
booktitle = {IEEE 21st International Workshop on Multimedia Signal Processing (MMSP 2019)}
}

Acknowledgment

This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under the Grant Number 15/RP/2776.