# Learning Light Field Angular Super-Resolution via a Geometry-Aware Network

arXiv: https://arxiv.org/abs/2002.11263 · HTML: https://mlsanity.com/paper/2002.11263/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2020.

## Built on by (most cited first, 16 of 16)

- [Disentangling Light Fields for Super-Resolution and Disparity Estimation](https://mlsanity.com/paper/2202.10603/) (2022)
- [Probabilistic-based Feature Embedding of 4-D Light Fields for Compressive Imaging and Denoising](https://mlsanity.com/paper/2306.08836/) (2023)
- [Light Field Reconstruction via Deep Adaptive Fusion of Hybrid Lenses](https://mlsanity.com/paper/2102.07085/) (2021)
- [Learning Dynamic Interpolation for Extremely Sparse Light Fields with Wide Baselines](https://mlsanity.com/paper/2108.07408/) (2021)
- [Light Field Implicit Representation for Flexible Resolution Reconstruction](https://mlsanity.com/paper/2112.00185/) (2021)
- [Unsupervised Light Field Depth Estimation via Multi-view Feature Matching with Occlusion Prediction](https://mlsanity.com/paper/2301.08433/) (2023)
- [Learning Texture Transformer Network for Light Field Super-Resolution](https://mlsanity.com/paper/2210.09293/) (2022)
- [Light Field Raindrop Removal via 4D Re-sampling](https://mlsanity.com/paper/2205.13165/) (2022)
- [Geo-NI: Geometry-aware Neural Interpolation for Light Field Rendering](https://mlsanity.com/paper/2206.09736/) (2022)
- [Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2509.22692/) (2025)
- [Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging](https://mlsanity.com/paper/2207.11894/) (2022)
- [Physics-Informed Ensemble Representation for Light-Field Image Super-Resolution](https://mlsanity.com/paper/2305.20006/) (2023)
- [Learning based Deep Disentangling Light Field Reconstruction and Disparity Estimation Application](https://mlsanity.com/paper/2311.08129/) (2023)
- [Self-supervised Learning-based Reconstruction of High-resolution 4D Light Fields](https://mlsanity.com/paper/2402.19020/) (2024)
- [Light Field Spatial Resolution Enhancement Framework](https://mlsanity.com/paper/2405.02787/) (2024)
- [Inverse Image-Based Rendering for Light Field Generation from Single Images](https://mlsanity.com/paper/2510.20132/) (2025)

## Abstract (arXiv)

The acquisition of light field images with high angular resolution is costly. Although many methods have been proposed to improve the angular resolution of a sparsely-sampled light field, they always focus on the light field with a small baseline, which is captured by a consumer light field camera. By making full use of the intrinsic textitgeometry information of light fields, in this paper we propose an end-to-end learning-based approach aiming at angularly super-resolving a sparsely-sampled light field with a large baseline. Our model consists of two learnable modules and a physically-based module. Specifically, it includes a depth estimation module for explicitly modeling the scene geometry, a physically-based warping for novel views synthesis, and a light field blending module specifically designed for light field reconstruction. Moreover, we introduce a novel loss function to promote the preservation of the light field parallax structure. Experimental results over various light field datasets including large baseline light field images demonstrate the significant superiority of our method when compared with state-of-the-art ones, i.e., our method improves the PSNR of the second best method up to 2 dB in average, while saves the execution time 48×. In addition, our method preserves the light field parallax structure better.