A learned fast-forward operator accelerates iterative ptychographic reconstruction by over twofold in wall-clock time while maintaining comparable quality on temporally held-out experimental data.
Further improvements to the ptychographical iterative engine.Optica, 4(7):736–745, 2017
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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A joint variational framework integrates ptychography and fluorescence into a unified nonlinear least-squares problem for improved X-ray imaging reconstructions.
citing papers explorer
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Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction
A learned fast-forward operator accelerates iterative ptychographic reconstruction by over twofold in wall-clock time while maintaining comparable quality on temporally held-out experimental data.
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A Joint Variational Framework for Multimodal X-ray Ptychography and Fluorescence Reconstruction
A joint variational framework integrates ptychography and fluorescence into a unified nonlinear least-squares problem for improved X-ray imaging reconstructions.