# Light Field Super-Resolution Via Graph-Based Regularization

arXiv: https://arxiv.org/abs/1701.02141 · HTML: https://mlsanity.com/paper/1701.02141/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2017.

## Built on by (most cited first, 15 of 15)

- [Disentangling Light Fields for Super-Resolution and Disparity Estimation](https://mlsanity.com/paper/2202.10603/) (2022)
- [Detail-Preserving Transformer for Light Field Image Super-Resolution](https://mlsanity.com/paper/2201.00346/) (2022)
- [Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution](https://mlsanity.com/paper/2302.08058/) (2023)
- [Dense Dual-Attention Network for Light Field Image Super-Resolution](https://mlsanity.com/paper/2110.12114/) (2021)
- [LFMamba: Light Field Image Super-Resolution with State Space Model](https://mlsanity.com/paper/2406.12463/) (2024)
- [Super-resolution 3D Human Shape from a Single Low-Resolution Image](https://mlsanity.com/paper/2208.10738/) (2022)
- [Light Field Reconstruction via Deep Adaptive Fusion of Hybrid Lenses](https://mlsanity.com/paper/2102.07085/) (2021)
- [Texture-enhanced Light Field Super-resolution with Spatio-Angular Decomposition Kernels](https://mlsanity.com/paper/2111.04069/) (2021)
- [Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolution](https://mlsanity.com/paper/2401.00740/) (2024)
- [3DVSR: 3D EPI Volume-based Approach for Angular and Spatial Light field Image Super-resolution](https://mlsanity.com/paper/2201.01294/) (2022)
- [MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-Resolution](https://mlsanity.com/paper/2410.06478/) (2024)
- [A GPU-Accelerated Light-field Super-resolution Framework Based on Mixed Noise Model and Weighted Regularization](https://mlsanity.com/paper/2206.05047/) (2022)
- [Physics-Informed Ensemble Representation for Light-Field Image Super-Resolution](https://mlsanity.com/paper/2305.20006/) (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)

## Abstract (arXiv)

Light field cameras capture the 3D information in a scene with a single exposure. This special feature makes light field cameras very appealing for a variety of applications: from post-capture refocus, to depth estimation and image-based rendering. However, light field cameras suffer by design from strong limitations in their spatial resolution, which should therefore be augmented by computational methods. On the one hand, off-the-shelf single-frame and multi-frame super-resolution algorithms are not ideal for light field data, as they do not consider its particular structure. On the other hand, the few super-resolution algorithms explicitly tailored for light field data exhibit significant limitations, such as the need to estimate an explicit disparity map at each view. In this work we propose a new light field super-resolution algorithm meant to address these limitations. We adopt a multi-frame alike super-resolution approach, where the complementary information in the different light field views is used to augment the spatial resolution of the whole light field. We show that coupling the multi-frame approach with a graph regularizer, that enforces the light field structure via nonlocal self similarities, permits to avoid the costly and challenging disparity estimation step for all the views. Extensive experiments show that the new algorithm compares favorably to the other state-of-the-art methods for light field super-resolution, both in terms of PSNR and visual quality.