# Physics Constrained Unsupervised Deep Learning for Rapid, High Resolution Scanning Coherent Diffraction Reconstruction

arXiv: https://arxiv.org/abs/2306.11014 · HTML: https://mlsanity.com/paper/2306.11014/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale Bragg Coherent Diffraction Imaging](https://mlsanity.com/paper/2109.14053/) (2021)

## Built on by (most cited first, 13 of 13)

- [Predicting ptychography probe positions using single-shot phase retrieval neural network](https://mlsanity.com/paper/2405.20910/) (2024)
- [Improving Multislice Electron Ptychography with a Generative Prior](https://mlsanity.com/paper/2507.17800/) (2025)
- [Deep learning phase recovery: data-driven, physics-driven, or combining both?](https://mlsanity.com/paper/2404.01360/) (2024)
- [DONUT: Physics-aware Machine Learning for Real-time X-ray Nanodiffraction Analysis](https://mlsanity.com/paper/2507.14038/) (2025)
- [Learning neural representations for X-ray ptychography reconstruction with unknown probes](https://mlsanity.com/paper/2509.04402/) (2025)
- [Fidelity-preserving enhancement of ptychography with foundational text-to-image models](https://mlsanity.com/paper/2509.04513/) (2025)
- [Towards generalizable deep ptychography neural networks](https://mlsanity.com/paper/2509.25104/) (2025)
- [A Start To End Machine Learning Approach To Maximize Scientific Throughput From The LCLS-II-HE](https://mlsanity.com/paper/2505.23858/) (2025)
- [Physics-Guided Machine Learning for Uncertainty Quantification in Turbulence Models](https://mlsanity.com/paper/2511.05633/) (2025)
- [A unified self-supervised framework for single-frame Fresnel CDI and overlapped ptychography](https://mlsanity.com/paper/2602.21361/) (2026)
- [Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction](https://mlsanity.com/paper/2605.01122/) (2026)
- [Contrast-invariant deep ptychography neural networks](https://mlsanity.com/paper/2608.02869/) (2026)
- [Zero-Shot Cross-Material Ptychographic Phase Reconstruction Using Deep Learning](https://mlsanity.com/paper/2609.13969/) (2026)

## Abstract (arXiv)

By circumventing the resolution limitations of optics, coherent diffractive imaging (CDI) and ptychography are making their way into scientific fields ranging from X-ray imaging to astronomy. Yet, the need for time consuming iterative phase recovery hampers real-time imaging. While supervised deep learning strategies have increased reconstruction speed, they sacrifice image quality. Furthermore, these methods' demand for extensive labeled training data is experimentally burdensome. Here, we propose an unsupervised physics-informed neural network reconstruction method, PtychoPINN, that retains the factor of 100-to-1000 speedup of deep learning-based reconstruction while improving reconstruction quality by combining the diffraction forward map with real-space constraints from overlapping measurements. In particular, PtychoPINN significantly advances generalizability, accuracy (with a typical 10 dB PSNR increase), and linear resolution (2- to 6-fold gain). This blend of performance and speed offers exciting prospects for high-resolution real-time imaging in high-throughput environments such as X-ray free electron lasers (XFELs) and diffraction-limited light sources.