# Learning Texture Transformer Network for Light Field Super-Resolution

arXiv: https://arxiv.org/abs/2210.09293 · HTML: https://mlsanity.com/paper/2210.09293/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 9 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Learning Texture Transformer Network for Image Super-Resolution](https://mlsanity.com/paper/2006.04139/) (2020)
- [Learning-Based View Synthesis for Light Field Cameras](https://mlsanity.com/paper/1609.02974/) (2016)
- [Learning Light Field Angular Super-Resolution via a Geometry-Aware Network](https://mlsanity.com/paper/2002.11263/) (2020)
- [Hybrid Light Field Imaging for Improved Spatial Resolution and Depth Range](https://mlsanity.com/paper/1611.05008/) (2016)

## Built on by (most cited first, 2 of 2)

- [Trade-off Between Spatial and Angular Resolution in Facial Recognition](https://mlsanity.com/paper/2402.07263/) (2024)
- [Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2509.22692/) (2025)

## Abstract (arXiv)

Hand-held light field cameras suffer from low spatial resolution due to the inherent spatio-angular tradeoff. In this paper, we propose a method to improve the spatial resolution of light field images with the aid of the Texture Transformer Network (TTSR). The proposed method consists of three modules: the first module produces an all-in focus high-resolution perspective image which serves as a reference image for the second module, i.e. TTSR, which in turn produces a high-resolution light field. The last module refines the spatial resolution by imposing a light field prior. The results demonstrate around 4 dB to 6 dB PSNR gain over a bicubically resized light field image