Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction

TL;DR

This paper introduces a machine learning-augmented method that significantly accelerates iterative ptychographic reconstruction, maintaining accuracy while reducing convergence time, and demonstrates its practical application in real-time experimental settings.

Contribution

It proposes a learned fast-forward operator that accelerates ptychographic reconstruction without sacrificing physical consistency, trained on diverse datasets and validated on experimental data.

Findings

Achieves over two-fold reduction in reconstruction time compared to traditional methods.

Maintains comparable reconstruction quality with faster convergence.

Successfully deployed in a real-world synchrotron beamline for real-time imaging.

Abstract

Iterative ptychographic reconstruction algorithms are widely used for coherent diffractive imaging but can exhibit slow convergence under realistic experimental conditions. We propose a machine learning-augmented approach that accelerates iterative ptychographic reconstruction by introducing a learned fast-forward operator applied during reconstruction. Following an initial warm-up using standard iterations, the fast-forward operator advances the reconstruction toward a more converged state, after which conventional iterative updates are resumed. This strategy preserves the physical consistency and flexibility of established ptychographic solvers while reducing the number of iterations required for convergence. The model is trained on diverse ptychographic datasets and evaluated on experimental data acquired in a different year, demonstrating robustness and temporal generalization. Compared with conventional iterative solvers, the machine learning-augmented method achieves comparable reconstruction quality while converging faster in terms of Poisson negative log-likelihood, yielding over a two-fold reduction in wall-clock time. The approach has been integrated into an existing reconstruction pipeline and deployed in production at a synchrotron beamline, demonstrating practicality for real-time experimental operation.