# Light Field Image Super-Resolution with Transformers

arXiv: https://arxiv.org/abs/2108.07597 · HTML: https://mlsanity.com/paper/2108.07597/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Learning Continuous Image Representation with Local Implicit Image Function](https://mlsanity.com/paper/2012.09161/) (2020)
- [Video Super-Resolution Transformer](https://mlsanity.com/paper/2106.06847/) (2021)
- [End-to-End Object Detection with Adaptive Clustering Transformer](https://mlsanity.com/paper/2011.09315/) (2020)
- [Spatial-Angular Interaction for Light Field Image Super-Resolution](https://mlsanity.com/paper/1912.07849/) (2019)
- [Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization](https://mlsanity.com/paper/2004.02215/) (2020)
- [Light Field Image Super-Resolution Using Deformable Convolution](https://mlsanity.com/paper/2007.03535/) (2020)
- [High-dimensional Dense Residual Convolutional Neural Network for Light Field Reconstruction](https://mlsanity.com/paper/1910.01426/) (2019)
- [Selective Light Field Refocusing for Camera Arrays Using Bokeh Rendering and Superresolution](https://mlsanity.com/paper/2108.03918/) (2021)
- [DeOccNet: Learning to See Through Foreground Occlusions in Light Fields](https://mlsanity.com/paper/1912.04459/) (2019)

## Built on by (most cited first, 30 of 31)

- [Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution](https://mlsanity.com/paper/2302.08058/) (2023)
- [LRT: An Efficient Low-Light Restoration Transformer for Dark Light Field Images](https://mlsanity.com/paper/2209.02197/) (2022)
- [Incorporating Degradation Estimation in Light Field Spatial Super-Resolution](https://mlsanity.com/paper/2405.07012/) (2024)
- [Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy](https://mlsanity.com/paper/2303.09190/) (2023)
- [Real-World Light Field Image Super-Resolution via Degradation Modulation](https://mlsanity.com/paper/2206.06214/) (2022)
- [NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results](https://mlsanity.com/paper/2304.10415/) (2023)
- [LFMamba: Light Field Image Super-Resolution with State Space Model](https://mlsanity.com/paper/2406.12463/) (2024)
- [Probabilistic-based Feature Embedding of 4-D Light Fields for Compressive Imaging and Denoising](https://mlsanity.com/paper/2306.08836/) (2023)
- [Co-visual pattern augmented generative transformer learning for automobile geo-localization](https://mlsanity.com/paper/2203.09135/) (2022)
- [Fast-FNet: Accelerating Transformer Encoder Models via Efficient Fourier Layers](https://mlsanity.com/paper/2209.12816/) (2022)
- [LFSRDiff: Light Field Image Super-Resolution via Diffusion Models](https://mlsanity.com/paper/2311.16517/) (2023)
- [UIERL: Internal-External Representation Learning Network for Underwater Image Enhancement](https://mlsanity.com/paper/2306.08344/) (2023)
- [Toward Real-World Light Field Super-Resolution](https://mlsanity.com/paper/2305.18994/) (2023)
- [Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolution](https://mlsanity.com/paper/2401.00740/) (2024)
- [LGFN: Lightweight Light Field Image Super-Resolution using Local Convolution Modulation and Global Attention Feature Extraction](https://mlsanity.com/paper/2409.17759/) (2024)
- [L2FMamba: Lightweight Light Field Image Super-Resolution with State Space Model](https://mlsanity.com/paper/2503.19253/) (2025)
- [Physics-enhanced Neural Operator for Simulating Turbulent Transport](https://mlsanity.com/paper/2406.04367/) (2024)
- [MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-Resolution](https://mlsanity.com/paper/2410.06478/) (2024)
- [Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2509.22692/) (2025)
- [Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement](https://mlsanity.com/paper/2304.12130/) (2023)
- [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)
- [MDeRainNet: An Efficient Macro-pixel Image Rain Removal Network](https://mlsanity.com/paper/2406.10652/) (2024)
- [Less is More: Skim Transformer for Light Field Image Super-resolution](https://mlsanity.com/paper/2407.15329/) (2024)
- [Modeling Continuous Spatial-temporal Dynamics of Turbulent Flow with Test-time Refinement](https://mlsanity.com/paper/2412.19927/) (2024)
- [Exploring Non-Local Spatial-Angular Correlations with a Hybrid Mamba-Transformer Framework for Light Field Super-Resolution](https://mlsanity.com/paper/2509.04824/) (2025)
- [BrainPuzzle: Hybrid Physics and Data-Driven Reconstruction for Transcranial Ultrasound Tomography](https://mlsanity.com/paper/2510.20029/) (2025)
- [RASLF: Representation-Aware State Space Model for Light Field Super-Resolution](https://mlsanity.com/paper/2603.16243/) (2026)
- [GTF: Omnidirectional EPI Transformer for Light Field Super-Resolution](https://mlsanity.com/paper/2605.04581/) (2026)
- [VSANet: View-aware Sparse Attention Network for Light Field Image Denoising](https://mlsanity.com/paper/2606.24737/) (2026)

## Abstract (arXiv)

Light field (LF) image super-resolution (SR) aims at reconstructing high-resolution LF images from their low-resolution counterparts. Although CNN-based methods have achieved remarkable performance in LF image SR, these methods cannot fully model the non-local properties of the 4D LF data. In this paper, we propose a simple but effective Transformer-based method for LF image SR. In our method, an angular Transformer is designed to incorporate complementary information among different views, and a spatial Transformer is developed to capture both local and long-range dependencies within each sub-aperture image. With the proposed angular and spatial Transformers, the beneficial information in an LF can be fully exploited and the SR performance is boosted. We validate the effectiveness of our angular and spatial Transformers through extensive ablation studies, and compare our method to recent state-of-the-art methods on five public LF datasets. Our method achieves superior SR performance with a small model size and low computational cost. Code is available at https://github.com/ZhengyuLiang24/LFT.