Learning a Domain-Specialized Network for Light Field Spatial-Angular Super-Resolution

Light field (LF) imaging is inherently constrained by the trade-off between spatial resolution and angular sampling density. To overcome this obstacle, spatial-angular super-resolution (SR) methods have been developed to achieve concurrent enhancement in both dimensions. Traditional spatial-angular SR methods treat spatial and angular SR as separate tasks, resulting in parameter redundancy and error accumulation. While recent end-to-end approaches attempt joint processing, their uniform treatment of these distinct problems overlooks critical domain-specific requirements. To address these challenges, we propose a domain-specialized framework that deploys stage-tailored strategies to satisfy domain-specific demands. Specifically, in the angular SR stage, we introduce a cross-view consistency modulation module that enhances inter-view coherence through long-range dependency modeling of angular features. In the spatial SR stage, we propose a detail-aware state space model to reconstruct fine-grained detail. Finally, we develop a cross-domain integration module that explores spatial-angular correlations by fusing multi-representational features from both domains to foster synergistic optimization. Experimental results on public LF datasets demonstrate substantial improvements over state-of-the-art methods in both qualitative and quantitative comparisons, with approximately 50% fewer model parameters compared to competing methods.