Towards generalizable deep ptychography neural networks
This addresses the need for real-time feedback in X-ray ptychography at next-generation light sources, offering a domain-specific incremental improvement in generalization.
The paper tackles the lack of robustness in deep neural networks for X-ray ptychography across diverse experimental conditions by proposing an unsupervised training workflow that emphasizes probe learning with synthetic objects, enabling a single network to generalize across multiple beamlines with reconstruction fidelity comparable to models trained on experimental data.
X-ray ptychography is a data-intensive imaging technique expected to become ubiquitous at next-generation light sources delivering many-fold increases in coherent flux. The need for real-time feedback under accelerated acquisition rates motivates surrogate reconstruction models like deep neural networks, which offer orders-of-magnitude speedup over conventional methods. However, existing deep learning approaches lack robustness across diverse experimental conditions. We propose an unsupervised training workflow emphasizing probe learning by combining experimentally-measured probes with synthetic, procedurally generated objects. This probe-centric approach enables a single physics-informed neural network to reconstruct unseen experiments across multiple beamlines; among the first demonstrations of multi-probe generalization. We find probe learning is equally important as in-distribution learning; models trained using this synthetic workflow achieve reconstruction fidelity comparable to those trained exclusively on experimental data, even when changing the type of synthetic training object. The proposed approach enables training of experiment-steering models that provide real-time feedback under dynamic experimental conditions.