Ptychography With PFT

Application of the Partial Fourier Transform (PFT) for the ptychography problem.

FFT (512 x 512)	PFT (128 x 128)

In addition to ptychography experiments utilizing the PFT, this repository provides a PyTorch implementation of the PFT found in https://github.com/snudatalab/PFT originally implemented in C++.

This repository also provides a Pytorch implementation of the more recent Auto-MPFT found in https://github.com/snudatalab/Auto-MPFT/tree/main originally implemented in C++. However, the experiments in this repository make use exclusively of the two-dimensional PFT introduced in the original work.

Associated Publications

Fast Partial Fourier Transforms for Large-Scale Ptychography

Full-Paper: https://www.aimsciences.org/article/doi/10.3934/ipi.2025019

Pre-Print: https://arxiv.org/abs/2408.03532

Please cite as

@misc{parada2024fastpartialfouriertransforms,
     title={Fast Partial Fourier Transforms for Large-Scale Ptychography}, 
     author={Ricardo Parada and Samy Wu Fung and Stanley Osher},
     year={2024},
     eprint={2408.03532},
     archivePrefix={arXiv},
     primaryClass={math.NA},
     url={https://arxiv.org/abs/2408.03532
     }

Set-up

Install all the requirments (designed for python 3.12.7)

pip install -r requirements.txt

PFT Demonstration

For a detailed demonstration of the Fast Partial Fourier Transform, refer to PFT_demo.ipynb. Technical and theoretical explanations can be found in https://arxiv.org/abs/2008.12559.

Experiments

Non-Blind Ptychography

Large-Scale PIE Experiment

python PIE_driver.py

or

python non_blind_experiment_large_scale.py

PIE Relative Errors Experiment

python nonblind_rel_err_experiments.py

Blind Ptychography

Large-Scale ePIE Experiment

python ePIE_driver.py

or

python ePIE_driver_large_scale.py

ePIE Relative Errors Experiment

python blind_rel_err_experiments.py

Acknowledgements

Samy Wu Fung was partially funded by National Science Foundation award DMS-2110745. Stanley Osher was partially funded by Air Force Office of Scientific Research (AFOSR) MURI FA9550-18-502, Office of Naval Research (ONR) N00014-20-1-2787, and STROBE: a National Science Foundation Science and Technology Center under Grant No. DMR-1548924. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.

We thank Yong-chan Park for his help on setting up the PyTorch-based PFT code.