PtychoNN: deep learning network for ptychographic imaging that predicts sample amplitude and phase from diffraction data.

A DL-based approach to solve the ptychography data inversion problem that learns a direct mapping from the reciprocal space data to the sample amplitude and phase.

Metadata

Model ptychonn.json

Datasets ptychonn_20191008_39.json

PtychoNN, uses a deep convolutional neural network to predict realspace structure and phase from far-field diffraction data. It recovers high fidelity amplitude and phase contrast images of a real sample hundreds of times faster than current ptychography reconstruction packages and reduces sampling requirements 1

References

  1. Mathew J. Cherukara, Tao Zhou, Youssef Nashed, Pablo Enfedaque, Alex Hexemer, Ross J. Harder, Martin V. Holt; AI-enabled high-resolution scanning coherent diffraction imaging. Appl. Phys. Lett. 27 July 2020; 117 (4): 044103. https://doi.org/10.1063/5.0013065 ↩︎

Last modified January 26, 2024: Fixed typos (a1d6fc4)