  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.
