Enhancing 4D Consistency for Mamba-Based Light Field Spatial Super-Resolution

Light field spatial super-resolution (LFSSR) relies on the comprehensive complementarity of 4D information. Existing CNN-based methods suffer from limited receptive field, while Transformer-based approaches incur prohibitive quadratic computational complexity. Although recent Mamba-based methods can model global dependency with linear complexity, the sequential scanning mechanism of Mamba inherently disrupts the intrinsic 4D local dependency of the light field image. Moreover, most of them encode the subspaces of light field in a decoupled manner, lacking explicit holistic modeling. In this paper, we propose LF Omnidirectional Mamba (LFOmniMamba) to address these challenges through two key components. First, the Domain-Enhanced Mamba (DEMamba) preserves and enhances local 4D consistency when processing light field subspaces via its internally integrated LF Neighbor-Guided Block. Second, the Pseudo-4D Mamba (P4DMamba) achieves efficient holistic global feature refinement through multi-scale 4D Mamba modeling. Building upon LFOmniMamba, we further propose LFOmniSR for comprehensive 4D feature extraction in LFSSR. Extensive benchmark experiments demonstrate that LFOmniSR outperforms state-of-the-art methods.