A geometry-aware implicit–explicit framework for light field angular super-resolution

Light field angular super-resolution (LFASR) aims to synthesize dense novel views from sparsely sampled light fields, thereby enabling immersive applications such as virtual and augmented reality. However, existing methods primarily focus on 2D cross-view interactions while insufficiently leveraging 3D scene geometry, leading to reduced angular consistency and suboptimal view synthesis-particularly in complex scenes. To address this limitation, we present an implicit–explicit hybrid representation and implement it within a geometry-aware implicit–explicit framework (GIENet) that enhances geometric reasoning for LFASR. Specifically, we propose an implicit geometry-aware interaction module, inspired by multi-plane image (MPI) representations, which captures geometry-aware features through structured interactions in a layered representation space. To overcome the representational constraints of a finite MPI stack, we further introduce an explicit geometric feature enhancement module that integrates self-inferred geometric priors into the reconstruction pathway. In addition, we propose an axis-aligned multi-plane aggregation module that fuses multi-view features along axis-aligned planes using separable convolutions, achieving both computational and memory efficiency while preserving fine details. Extensive experiments demonstrate that GIENet not only achieves superior reconstruction quality but also offers competitive computational efficiency and remarkable robustness across diverse upsampling factors and datasets.