[2412.09646] RealOSR: Latent Guidance Boosts Diffusion-based Real-world Omnidirectional Image Super-Resolutions

Electrical Engineering and Systems Science > Image and Video Processing arXiv:2412.09646 (eess) [Submitted on 11 Dec 2024 (v1), last revised 3 Mar 2026 (this version, v2)] Title:RealOSR: Latent Guidance Boosts Diffusion-based Real-world Omnidirectional Image Super-Resolutions Authors:Xuhan Sheng, Runyi Li, Bin Chen, Weiqi Li, Xu Jiang, Jian Zhang View a PDF of the paper titled RealOSR: Latent Guidance Boosts Diffusion-based Real-world Omnidirectional Image Super-Resolutions, by Xuhan Sheng and Runyi Li and Bin Chen and Weiqi Li and Xu Jiang and Jian Zhang View PDF Abstract:Omnidirectional image super-resolution (ODISR) aims to upscale low-resolution (LR) omnidirectional images (ODIs) to high-resolution (HR), catering to the growing demand for detailed visual content across a $ 180^{\circ}\times360^{\circ}$ viewport. Existing ODISR methods are limited by simplified degradation assumptions (e.g., bicubic downsampling), failing to model and exploit the real-world degradation information. Recent latent-based diffusion approaches using condition guidance suffer from slow inference due to their hundreds of updating steps and frequent use of VAE. To tackle these challenges, we propose \textbf{RealOSR}, a diffusion-based framework tailored for real-world ODISR, featuring efficient latent-based condition guidance within a one-step denoising paradigm. Central to efficient latent-based condition guidance is the proposed \textbf{Latent Gradient Alignment Routing (LaGAR)}, a lightweigh...