Contrast-invariant deep ptychography neural networks

Albert Vong; Steven Henke; Oliver Hoidn; Hanna Ruth; Junjing Deng; Apurva Mehta; David Shapiro; Alexander Hexemer; Nicholas Schwarz

arXiv · 2608.02869

Contrast-invariant deep ptychography neural networks

Albert Vong·Steven Henke·Oliver Hoidn·Hanna Ruth·Junjing Deng·Apurva Mehta·David Shapiro·Alexander Hexemer·Nicholas Schwarz

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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.

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Albert Vong, Steven Henke, Oliver Hoidn, Hanna Ruth, Junjing Deng, Apurva Mehta, David Shapiro, Alexander Hexemer, Nicholas Schwarz. 2026-08-03. Contrast-invariant deep ptychography neural networks. https://arxiv.org/abs/2608.02869

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