Research map / Image generation / Super-resolution
High-Order Residual Network for Light Field Super-Resolution
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the full angular information from all directions in learned geometric features instead of using only partial angular correlations | High-Order Residual Network · Spatial LFSR, ×2, ×3, and ×4 | PSNR | Helped |
| Combine reconstruction loss with perceptual loss instead of relying on a single loss component (ablation) | High-Order Residual Network · LFSR loss evaluation | quantitative results | Helped |
| Use hierarchical HRBs rather than 16 alternating 4D convolutions | Angular super-resolution model · Angular SR, 2×2 → 8×8 | PSNR | Helped |
Closest papers by meaning
- Residual Networks for Light Field Image Super-Resolution (CVPR 2019 · similarity 0.91)
- Occlusion-Embedded Hybrid Transformer for Light Field Super-Resolution (AAAI 2025 · similarity 0.86)
- From Pixels to Views: Learning Angular-Aware and Physics-Consistent Representations for Light Field Microscopy (NeurIPS 2025 · similarity 0.84)
- TurboSL: Dense Accurate and Fast 3D by Neural Inverse Structured Light (CVPR 2024 · similarity 0.84)
- 4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time (NeurIPS 2025 · similarity 0.84)
- Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real World (CVPR 2024 · similarity 0.84)
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