# OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model

arXiv: https://arxiv.org/abs/2404.10312 · HTML: https://mlsanity.com/paper/2404.10312/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- Builds on 18 of its 68 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Posterior Sampling for General Noisy Inverse Problems](https://mlsanity.com/paper/2209.14687/) (2022)
- [Denoising Diffusion Restoration Models](https://mlsanity.com/paper/2201.11793/) (2022)
- [Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model](https://mlsanity.com/paper/2212.00490/) (2022)
- [Improving Diffusion Models for Inverse Problems using Manifold Constraints](https://mlsanity.com/paper/2206.00941/) (2022)
- [Exploiting Diffusion Prior for Real-World Image Super-Resolution](https://mlsanity.com/paper/2305.07015/) (2023)
- [ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting](https://mlsanity.com/paper/2307.12348/) (2023)
- [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://mlsanity.com/paper/1706.08500/) (2017)
- [Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models](https://mlsanity.com/paper/2307.00619/) (2023)
- [Intermediate Layer Optimization for Inverse Problems using Deep Generative Models](https://mlsanity.com/paper/2102.07364/) (2021)
- [Prompt-tuning latent diffusion models for inverse problems](https://mlsanity.com/paper/2310.01110/) (2023)
- [Residual Denoising Diffusion Models](https://mlsanity.com/paper/2308.13712/) (2023)
- [CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography](https://mlsanity.com/paper/2305.16936/) (2023)
- [Regularization by Texts for Latent Diffusion Inverse Solvers](https://mlsanity.com/paper/2311.15658/) (2023)
- [D3C2-Net: Dual-Domain Deep Convolutional Coding Network for Compressive Sensing](https://mlsanity.com/paper/2207.13560/) (2022)
- [Cas-DiffCom: Cascaded diffusion model for infant longitudinal super-resolution 3D medical image completion](https://mlsanity.com/paper/2402.13776/) (2024)

## Built on by (most cited first, 11 of 11)

- [PANORAMA: The Rise of Omnidirectional Vision in the Embodied AI Era](https://mlsanity.com/paper/2509.12989/) (2025)
- [ResVR: Joint Rescaling and Viewport Rendering of Omnidirectional Images](https://mlsanity.com/paper/2404.16825/) (2024)
- [GaussianSeal: Rooting Adaptive Watermarks for 3D Gaussian Generation Model](https://mlsanity.com/paper/2503.00531/) (2025)
- [RealOSR: Latent Guidance Boosts Diffusion-based Real-world Omnidirectional Image Super-Resolutions](https://mlsanity.com/paper/2412.09646/) (2024)
- [Spatio-Temporal Distortion Aware Omnidirectional Video Super-Resolution](https://mlsanity.com/paper/2410.11506/) (2024)
- [A Systematic Investigation on Deep Learning-Based Omnidirectional Image and Video Super-Resolution](https://mlsanity.com/paper/2506.06710/) (2025)
- [STCDiT: Spatio-Temporally Consistent Diffusion Transformer for High-Quality Video Super-Resolution](https://mlsanity.com/paper/2511.18786/) (2025)
- [SupResDiffGAN a new approach for the Super-Resolution task](https://mlsanity.com/paper/2504.13622/) (2025)
- [LAFR: Efficient Diffusion-based Blind Face Restoration via Latent Codebook Alignment Adapter](https://mlsanity.com/paper/2505.23462/) (2025)
- [Multi-level distortion-aware deformable network for omnidirectional image super-resolution](https://mlsanity.com/paper/2512.17343/) (2025)
- [D2R2OSR: Degradation-Disentangled Representation for Real-World Omnidirectional Image Super-Resolution](https://mlsanity.com/paper/2606.29314/) (2026)

## Abstract (arXiv)

Omnidirectional images (ODIs) are commonly used in real-world visual tasks, and high-resolution ODIs help improve the performance of related visual tasks. Most existing super-resolution methods for ODIs use end-to-end learning strategies, resulting in inferior realness of generated images and a lack of effective out-of-domain generalization capabilities in training methods. Image generation methods represented by diffusion model provide strong priors for visual tasks and have been proven to be effectively applied to image restoration tasks. Leveraging the image priors of the Stable Diffusion (SD) model, we achieve omnidirectional image super-resolution with both fidelity and realness, dubbed as OmniSSR. Firstly, we transform the equirectangular projection (ERP) images into tangent projection (TP) images, whose distribution approximates the planar image domain. Then, we use SD to iteratively sample initial high-resolution results. At each denoising iteration, we further correct and update the initial results using the proposed Octadecaplex Tangent Information Interaction (OTII) and Gradient Decomposition (GD) technique to ensure better consistency. Finally, the TP images are transformed back to obtain the final high-resolution results. Our method is zero-shot, requiring no training or fine-tuning. Experiments of our method on two benchmark datasets demonstrate the effectiveness of our proposed method.