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Citation and History

If you use DeepLens in your research, please cite the relevant papers.

Primary Citation

Curriculum Learning for Ab Initio Deep Learned Refractive Optics

@article{yang2024curriculum,
  title={Curriculum learning for ab initio deep learned refractive optics},
  author={Yang, Xinge and Fu, Qiang and Heidrich, Wolfgang},
  journal={Nature Communications},
  volume={15},
  number={1},
  pages={6572},
  year={2024},
  publisher={Nature Publishing Group UK London}
}

Paper link: https://www.nature.com/articles/s41467-024-50835-7

Hybrid Lens Design

dflens: Differentiable Pipeline for Hybrid Refractive-Diffractive Lens Design

@article{wang2024dflens,
  title={dflens: A Differentiable Pipeline for Hybrid Refractive-Diffractive Lens Design},
  author={Wang, Congli and Chen, Ni and Heidrich, Wolfgang},
  journal={arXiv preprint arXiv:2406.00834},
  year={2024}
}

Paper link: https://arxiv.org/abs/2406.00834

Aberration-Aware Depth Estimation

Aberration-Aware Depth-from-Focus

@article{yang2023aberration,
  title={Aberration-aware depth-from-focus},
  author={Yang, Xinge and Fu, Qiang and Heidrich, Wolfgang},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={46},
  number={3},
  pages={1697--1711},
  year={2023},
  publisher={IEEE}
}

Paper link: https://ieeexplore.ieee.org/document/10209238

History of DeepLens

Evolution Timeline

2021: Early Development

  • Initial concept and prototyping
  • Basic ray tracing framework
  • Differentiable optimization proof-of-concept

2022: Core Features

  • Comprehensive optical surface library
  • Wave optics integration
  • Sensor and ISP simulation
  • First automated lens designs

2023: Advanced Capabilities

  • Aberration-aware imaging (TPAMI 2023)
  • Neural surrogate models (PSFNet)
  • End-to-end optimization framework
  • Multi-wavelength support

2024: Public Release

  • Nature Communications publication
  • Open-source release
  • Hybrid refractive-diffractive support (SIGGRAPH Asia 2024)
  • Community building

2025: Ongoing Development

  • Enhanced GPU acceleration
  • Distributed computing support
  • Polarization ray tracing
  • Non-sequential systems

Key Milestones

Date Milestone
2021 Project initiated at KAUST
2022 First fully automated lens design from scratch
2023 Jun TPAMI paper on aberration-aware depth estimation
2024 Jul Nature Communications paper published
2024 Aug Open-source release on GitHub
2024 Dec SIGGRAPH Asia paper on hybrid lenses
2025 Community edition with extended features

Contributors

Core Team

  • Xinge Yang - Project Lead, Main Developer
  • Qiang Fu - Optical Design Consultant
  • Wolfgang Heidrich - Principal Investigator

Major Contributors

  • Congli Wang - Hybrid lens module
  • Ni Chen - Wave optics algorithms

See full list at: https://github.com/singer-yang/DeepLens/graphs/contributors

Acknowledgments

Funding

This project was supported by:

  • King Abdullah University of Science and Technology (KAUST)
  • Visual Computing Center (VCC)
  • Computational Imaging Lab

Collaborations

We thank our collaborators:

  • MIT Media Lab
  • Stanford Computational Imaging Lab
  • Various industry partners

Open Source Community

Special thanks to our open-source community for:

  • Bug reports and fixes
  • Feature suggestions
  • Documentation improvements
  • Testing and validation

Awards and Recognition

  • Nature Communications - Featured article (2024)
  • SIGGRAPH Asia - Technical paper (2024)
  • TPAMI - Journal publication (2023)

Media Coverage

DeepLens has been featured in:

  • Nature Communications press release
  • KAUST Discovery magazine
  • Computer graphics and optics news outlets

Research Using DeepLens

If you have published research using DeepLens, let us know! We'd love to feature it here.

Example applications:

  • Automated lens design for imaging systems
  • Computational photography research
  • Virtual reality optics
  • Smartphone camera optimization
  • Microscopy and scientific imaging
  • Automotive camera systems

Contact

For academic collaborations:

  • Email: xinge.yang@kaust.edu.sa
  • Lab: Visual Computing Center, KAUST

For commercial licensing:

  • Email: wolfgang.heidrich@kaust.edu.sa
  • Website: https://vccimaging.org

Community

Join our community:

  • GitHub: https://github.com/singer-yang/DeepLens
  • Slack: https://join.slack.com/t/deeplens/shared_invite/zt-2wz3x2n3b-plRqN26eDhO2IY4r_gmjOw
  • WeChat: Contact singeryang1999

License

DeepLens is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).

What this means:

  • ✓ Use for academic and research purposes
  • ✓ Modify and build upon the work
  • ✓ Share with attribution
  • ✗ Commercial use without permission

For commercial licensing, please contact the authors.

Future Directions

Planned Features

  • Extended material database
  • Tolerance analysis tools
  • Manufacturing interface
  • Cloud-based optimization
  • Real-time preview
  • VR/AR integration

Research Directions

  • Quantum optical systems
  • Freeform optics
  • Holographic displays
  • Neuromorphic imaging
  • Computational microscopy

Get Involved

Ways to contribute:

  • Use DeepLens in your research
  • Report bugs and issues
  • Suggest new features
  • Contribute code
  • Write documentation
  • Share your designs

See Contributing for details.

See Also