Contrast-invariant deep ptychography neural networks

2026-08-03

AI Summary

Deep ptychographic imaging neural networks often suffer from illumination-induced scale inconsistencies during out-of-distribution generalization, undermining their practical reliability. To address this issue, this work proposes a scale-decoupled factorization strategy that reformulates object representation from the conventional amplitude–phase domain to a real–imaginary formulation. Additionally, a synthetic object sampling scheme is introduced to reduce the phase distribution discrepancy between synthetic and experimental data. This approach effectively disentangles texture from measurement scale, yielding substantial improvements in generalization across five cross-illumination experimental datasets. Compared to the PtychoPINN-torch baseline, the proposed method reduces Fourier error by up to fivefold.

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.

Problem

• ptychography
• scaling inconsistency
• out-of-distribution generalization
• measurement consistency
• neural networks

Innovation

• contrast-invariant
• ptychography
• scale decoupling
• real-imaginary representation
• synthetic object sampling