让显微成像神经网络适应不同光照，重建更准确。
Contrast-invariant deep ptychography neural networks
- 分离物体纹理与测量尺度，实现光照不变的重建
- 在5个实验数据集上傅里叶误差降低5倍
- 适合需要跨条件通用的显微成像研究者
相位衬度显微成像中的神经网络在分布外泛化时存在尺度不一致问题，限制了实际应用。本文提出一种分解策略，将学习到的物体纹理与测量尺度解耦，使单个训练好的网络能在不同光照条件下生成与测量一致的重建结果。这要求以实部和虚部单位预测物体，而非传统的振幅和相位表示。同时引入一种合成物体采样策略，减少合成训练数据与真实实验目标之间的相位分布差异。这些改进在涵盖多个束线和设施的5个实验数据集上，使傅里叶误差相比之前的PtychoPINN-torch基线最多降低5倍。
原文摘要 · Abstract (English)
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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