# Super-resolution of Omnidirectional Images Using Adversarial Learning

arXiv: https://arxiv.org/abs/1908.04297 · HTML: https://mlsanity.com/paper/1908.04297/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2019.

## Built on by (most cited first, 18 of 18)

- [NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results](https://mlsanity.com/paper/2404.09790/) (2024)
- [Deep Learning for Omnidirectional Vision: A Survey and New Perspectives](https://mlsanity.com/paper/2205.10468/) (2022)
- [SphereSR: 360deg Image Super-Resolution with Arbitrary Projection via Continuous Spherical Image Representation](https://mlsanity.com/paper/2112.06536/) (2021)
- [OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer](https://mlsanity.com/paper/2302.03453/) (2023)
- [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://mlsanity.com/paper/2502.10444/) (2025)
- [OmniZoomer: Learning to Move and Zoom in on Sphere at High-Resolution](https://mlsanity.com/paper/2308.08114/) (2023)
- [ResVR: Joint Rescaling and Viewport Rendering of Omnidirectional Images](https://mlsanity.com/paper/2404.16825/) (2024)
- [Geometric Distortion Guided Transformer for Omnidirectional Image Super-Resolution](https://mlsanity.com/paper/2406.10869/) (2024)
- [RealOSR: Latent Guidance Boosts Diffusion-based Real-world Omnidirectional Image Super-Resolutions](https://mlsanity.com/paper/2412.09646/) (2024)
- [Fast Omni-Directional Image Super-Resolution: Adapting the Implicit Image Function with Pixel and Semantic-Wise Spherical Geometric Priors](https://mlsanity.com/paper/2502.05902/) (2025)
- [Spatio-Temporal Distortion Aware Omnidirectional Video Super-Resolution](https://mlsanity.com/paper/2410.11506/) (2024)
- [Quality Assessment of Super-Resolved Omnidirectional Image Quality Using Tangential Views](https://mlsanity.com/paper/2101.10396/) (2021)
- [Learning High-Quality Navigation and Zooming on Omnidirectional Images in Virtual Reality](https://mlsanity.com/paper/2405.00351/) (2024)
- [A Systematic Investigation on Deep Learning-Based Omnidirectional Image and Video Super-Resolution](https://mlsanity.com/paper/2506.06710/) (2025)
- [Omnidirectional Video Super-Resolution using Deep Learning](https://mlsanity.com/paper/2506.14803/) (2025)
- [Distortion-Aware Self-Supervised 360deg Depth Estimation from A Single Equirectangular Projection Image](https://mlsanity.com/paper/2204.01027/) (2022)
- [360^circ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer](https://mlsanity.com/paper/2304.07967/) (2023)
- [D2R2OSR: Degradation-Disentangled Representation for Real-World Omnidirectional Image Super-Resolution](https://mlsanity.com/paper/2606.29314/) (2026)

## Abstract (arXiv)

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.