An Omnidirectional Image Blind Super-Resolution Algorithm Based on Random Degradation Mechanism

Omnidirectional images (ODIs) are vital in VR and 360° imaging but suffer from quality degradation during acquisition and transmission. Existing blind SR methods often ignore multi-degradation coupling and spherical geometry. We propose a blind SR algorithm combining a stochastic degradation model (blur, noise, JPEG compression) and an attention-enhanced dual-branch network with SE-guided residual dense blocks and a relativistic adversarial discriminator. The attention mechanism adaptively recalibrates spherical features, while dense connections enable hierarchical fusion across equatorial and polar regions. Evaluations on ODI-SR show significant PSNR/SSIM gains over baselines, with ablation studies validating the synergy of degradation modeling and architectural innovations in preserving high-frequency details and geometric consistency.