PtyRANNOSAUR uses convolutional autoencoders trained on crystal structure databases to map 4D-STEM ptychography data to sub-0.5 Å phase images 10-100x faster than iterative methods while handling partial coherence, multiple scattering, and scan errors.
Pty-chi: A pytorch-based modern ptychographic data analysis package
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
PtychoPINN is extended to overlap-free single-shot Fresnel CDI reconstructions that achieve SSIM 0.904 on experimental data while using fewer training images and running 40 times faster than traditional least-squares methods.
citing papers explorer
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PtyRANNOSAUR: Ptychography with Robust Artificial Neural Networks Optimized for Sub-Angstrom Accuracy and Ultrafast Reconstruction
PtyRANNOSAUR uses convolutional autoencoders trained on crystal structure databases to map 4D-STEM ptychography data to sub-0.5 Å phase images 10-100x faster than iterative methods while handling partial coherence, multiple scattering, and scan errors.
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Towards single-shot coherent imaging via overlap-free ptychography
PtychoPINN is extended to overlap-free single-shot Fresnel CDI reconstructions that achieve SSIM 0.904 on experimental data while using fewer training images and running 40 times faster than traditional least-squares methods.