Contrast-invariant deep ptychography neural networks

Abstract:Ptychography neural networks suffer from scaling inconsistencies when generalizing out of distribution, limiting their real world viability. We address this scaling mismatch using a factorization strategy which decouples the learned object texture from measurement scaling, enabling a single trained network to produce measurement-consistent reconstructions across varying illumination conditions. This requires predicting the learned object in real and imaginary units instead of the canonical amplitude and phase representation. We additionally introduce a synthetic object sampling strategy that minimizes phase distribution mismatch between synthetic training data and experimental targets. These improvements yield up to a 5x reduction in Fourier error over the previous PtychoPINN-torch baseline across 5 experimental datasets spanning multiple beamlines and facilities.

~30 pages, 4 main figures + 2 SI figures. Submitting to scientific journal

Machine Learning (cs.LG)

arXiv:2608.02869 [cs.LG]

(or arXiv:2608.02869v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.02869 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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