HyLF: Sparse Angular Super-resolution from Hybrid Light Fields without Known Camera Parameters

Light field imaging, with its ability to record both the intensity and direction of light rays, has enabled realistic simulation of focus distance variations for refocusing and novel view synthesis. However, traditional light field imaging technology depends on dense sampling, making it difficult to attain high spatial and angular resolutions simultaneously. To address these limitations, we propose hybrid light field (HyLF), a self-supervised framework that realizes sparse angular super-resolution without requiring prior knowledge of camera parameters. HyLF integrates an implicit neural light field with an explicit voxel representation to achieve high-quality angular super-resolution from sparse inputs. First, we propose a jointly optimized two-plane embedding strategy that effectively encodes each ray in the absence of camera parameters. Next, an implicit neural multiplanar light field models focus distance variations with embedded rays, extracting more features to address the challenge of sparse angular sampling. Finally, an explicit planar-prior voxel representation decomposes the multiplane features predicted by the implicit module into voxel grids across focus planes, isolating salient voxels to enhance angular super-resolution. Comprehensive experiments demonstrate that HyLF outperforms existing baselines, achieving higher quantitative metrics, clearer visual quality, and faster inference speed.