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NeOTF: Guidestar-free neural representation for broadband dynamic imaging through scattering

1University of California, Irvine, Nhu Department of Electrical Engineering and Computer Science, Irvine, California, United States
2University of California, Irvine, Beckman Laser Institute and Medical Clinic, Irvine, California, United States
*Corresponding authors; [email protected],[email protected]

arXiv Advanced Photonics Google Colab

🚩Accepted by Advanced Photonics 2026

🔥 News

  • [2026.04] 🎉🎉🎉 Congratulations! NeOTF has been accepted by Advanced Photonics.
  • [2025.12] The code repo is released on Github.
  • [2025.11] The preprint is available on arXiv.

🎬 Overview

overview

NeOTF is a guidestar-free OTF retrieval method for imaging through dynamic scattering media. By optimizing a neural representation with only a few speckle images from unknown objects, NeOTF robustly retrieves the system's OTF without a guidestar.

🔧 Dependencies and Installation

  1. Clone repo

    git clone https://github.com/Xia-Research-Lab/NeOTF.git
    cd NeOTF
  2. Install dependent packages

    conda create -n NeOTF python=3.10 -y
    conda activate NeOTF
    pip install torch numpy pillow matplotlib tqdm pyyaml

⚡ Quick Inference

For training and reconstructing images from default multi-frame speckles, simply run:

python NeOTF.py --config ./config.yml

Run all baseline methods (HIO+ER, MORE) alongside NeOTF:

bash run_main.sh --config config.yml --output_dir ./outputs

📷 Results

Mutliframe images are reconstructed from inverse filtering with the static OTF retrieved within NeOTF training. The NeOTF is visualized as below.

results

🧩 Repository Structure

  • NeOTF.py: Main NeOTF training and reconstruction pipeline.
  • MORE.py: MORE algorithm baseline.
  • HIOER.py: HIO+ER algorithm baseline.
  • SIREN.py: Neural network module.
  • utils.py: Data loading and helper functions.
  • config.yml: Default configuration file.
  • run_main.sh: Benchmark bash script.

🎓 Citations

If our code helps your research or work, please consider citing our paper.

@article{sun2026neotf,
  title={NeOTF: guidestar-free neural representation for broadband dynamic imaging through scattering},
  author={Sun, Yunong and Xia, Fei},
  journal={Advanced Photonics},
  volume={8},
  number={3},
  pages={036007--036007},
  year={2026},
  publisher={Society of Photo-Optical Instrumentation Engineers}
}

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[Adv. Photon. 2026] Code implementation of NeOTF: Guidestar-free neural representation for broadband dynamic imaging through scattering

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