# Detail-Preserving Transformer for Light Field Image Super-Resolution

arXiv: https://arxiv.org/abs/2201.00346 · HTML: https://mlsanity.com/paper/2201.00346/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 2 Jan 2022.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [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)
- [Learning Texture Transformer Network for Image Super-Resolution](https://mlsanity.com/paper/2006.04139/) (2020)
- [Exploring Cross-Image Pixel Contrast for Semantic Segmentation](https://mlsanity.com/paper/2101.11939/) (2021)
- [Video Super-Resolution Transformer](https://mlsanity.com/paper/2106.06847/) (2021)
- [Structure-Preserving Super Resolution with Gradient Guidance](https://mlsanity.com/paper/2003.13081/) (2020)
- [Motion-Attentive Transition for Zero-Shot Video Object Segmentation](https://mlsanity.com/paper/2003.04253/) (2020)
- [Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2012.05007/) (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)
- [COLA-Net: Collaborative Attention Network for Image Restoration](https://mlsanity.com/paper/2103.05961/) (2021)
- [Light Field Super-Resolution Via Graph-Based Regularization](https://mlsanity.com/paper/1701.02141/) (2017)

## Built on by (most cited first, 26 of 26)

- [Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution](https://mlsanity.com/paper/2302.08058/) (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)
- [Dense residual Transformer for image denoising](https://mlsanity.com/paper/2205.06944/) (2022)
- [Reciprocal Attention Mixing Transformer for Lightweight Image Restoration](https://mlsanity.com/paper/2305.11474/) (2023)
- [Probabilistic-based Feature Embedding of 4-D Light Fields for Compressive Imaging and Denoising](https://mlsanity.com/paper/2306.08836/) (2023)
- [LFSRDiff: Light Field Image Super-Resolution via Diffusion Models](https://mlsanity.com/paper/2311.16517/) (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)
- [Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2307.02974/) (2023)
- [Learning Kernel-Modulated Neural Representation for Efficient Light Field Compression](https://mlsanity.com/paper/2307.06143/) (2023)
- [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)
- [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)
- [Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging](https://mlsanity.com/paper/2207.11894/) (2022)
- [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)
- [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)
- [Slope-Guided Mamba and Angular-Refined Transformer for Light Field Super-Resolution](https://mlsanity.com/paper/2607.00965/) (2026)

## Abstract (arXiv)

Recently, numerous algorithms have been developed to tackle the problem of light field super-resolution (LFSR), i.e., super-resolving low-resolution light fields to gain high-resolution views. Despite delivering encouraging results, these approaches are all convolution-based, and are naturally weak in global relation modeling of sub-aperture images necessarily to characterize the inherent structure of light fields. In this paper, we put forth a novel formulation built upon Transformers, by treating LFSR as a sequence-to-sequence reconstruction task. In particular, our model regards sub-aperture images of each vertical or horizontal angular view as a sequence, and establishes long-range geometric dependencies within each sequence via a spatial-angular locally-enhanced self-attention layer, which maintains the locality of each sub-aperture image as well. Additionally, to better recover image details, we propose a detail-preserving Transformer (termed as DPT), by leveraging gradient maps of light field to guide the sequence learning. DPT consists of two branches, with each associated with a Transformer for learning from an original or gradient image sequence. The two branches are finally fused to obtain comprehensive feature representations for reconstruction. Evaluations are conducted on a number of light field datasets, including real-world scenes and synthetic data. The proposed method achieves superior performance comparing with other state-of-the-art schemes. Our code is publicly available at: https://github.com/BITszwang/DPT.