# Light Field Image Super-Resolution Using Deformable Convolution

arXiv: https://arxiv.org/abs/2007.03535 · HTML: https://mlsanity.com/paper/2007.03535/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

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

- [Disentangling Light Fields for Super-Resolution and Disparity Estimation](https://mlsanity.com/paper/2202.10603/) (2022)
- [Light Field Image Super-Resolution with Transformers](https://mlsanity.com/paper/2108.07597/) (2021)
- [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)
- [A Systematic Survey of Deep Learning-based Single-Image Super-Resolution](https://mlsanity.com/paper/2109.14335/) (2021)
- [Incorporating Degradation Estimation in Light Field Spatial Super-Resolution](https://mlsanity.com/paper/2405.07012/) (2024)
- [DehazeDCT: Towards Effective Non-Homogeneous Dehazing via Deformable Convolutional Transformer](https://mlsanity.com/paper/2407.05169/) (2024)
- [Real-World Light Field Image Super-Resolution via Degradation Modulation](https://mlsanity.com/paper/2206.06214/) (2022)
- [Dense Dual-Attention Network for Light Field Image Super-Resolution](https://mlsanity.com/paper/2110.12114/) (2021)
- [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)
- [LFSRDiff: Light Field Image Super-Resolution via Diffusion Models](https://mlsanity.com/paper/2311.16517/) (2023)
- [Light Field Implicit Representation for Flexible Resolution Reconstruction](https://mlsanity.com/paper/2112.00185/) (2021)
- [Toward Real-World Light Field Super-Resolution](https://mlsanity.com/paper/2305.18994/) (2023)
- [I See-Through You: A Framework for Removing Foreground Occlusion in Both Sparse and Dense Light Field Images](https://mlsanity.com/paper/2301.06392/) (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)
- [ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation](https://mlsanity.com/paper/2103.09699/) (2021)
- [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)
- [View Adaptive Light Field Deblurring Networks with Depth Perception](https://mlsanity.com/paper/2303.06860/) (2023)
- [Physics-Informed Ensemble Representation for Light-Field Image Super-Resolution](https://mlsanity.com/paper/2305.20006/) (2023)
- [Exploring Non-Local Spatial-Angular Correlations with a Hybrid Mamba-Transformer Framework for Light Field Super-Resolution](https://mlsanity.com/paper/2509.04824/) (2025)
- [A Linear Fractional Transformation Model and Calibration Method for Light Field Camera](https://mlsanity.com/paper/2511.03962/) (2025)
- [RASLF: Representation-Aware State Space Model for Light Field Super-Resolution](https://mlsanity.com/paper/2603.16243/) (2026)
- [HistoFusionNet: Histogram-Guided Fusion and Frequency-Adaptive Refinement for Nighttime Image Dehazing](https://mlsanity.com/paper/2604.03800/) (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)
- [Enhanced Deformable Convolution with Center-invariant Offset and Edge-aware Mask](https://mlsanity.com/paper/2609.10387/) (2026)

## Abstract (arXiv)

Light field (LF) cameras can record scenes from multiple perspectives, and thus introduce beneficial angular information for image super-resolution (SR). However, it is challenging to incorporate angular information due to disparities among LF images. In this paper, we propose a deformable convolution network (i.e., LF-DFnet) to handle the disparity problem for LF image SR. Specifically, we design an angular deformable alignment module (ADAM) for feature-level alignment. Based on ADAM, we further propose a collect-and-distribute approach to perform bidirectional alignment between the center-view feature and each side-view feature. Using our approach, angular information can be well incorporated and encoded into features of each view, which benefits the SR reconstruction of all LF images. Moreover, we develop a baseline-adjustable LF dataset to evaluate SR performance under different disparity variations. Experiments on both public and our self-developed datasets have demonstrated the superiority of our method. Our LF-DFnet can generate high-resolution images with more faithful details and achieve state-of-the-art reconstruction accuracy. Besides, our LF-DFnet is more robust to disparity variations, which has not been well addressed in literature.