A deep learning framework represents phase on the unit circle with a geodesic loss for improved ptychographic amplitude and phase reconstruction.
Deep-learning electron diffractive imaging
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
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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Circular Phase Representation and Geometry-Aware Optimization for Ptychographic Image Reconstruction
A deep learning framework represents phase on the unit circle with a geodesic loss for improved ptychographic amplitude and phase reconstruction.
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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.