Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction

arXiv cs.LG / 5/5/2026

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Key Points

The paper proposes a machine learning–augmented iterative ptychographic reconstruction method that uses a learned fast-forward operator to speed up convergence under realistic experimental conditions.
The workflow uses a brief warm-up with standard ptychographic iterations, then applies the learned operator to move the solution closer to a converged state before switching back to conventional iterative updates.
Training on diverse ptychographic datasets and testing on experimental data from a different year show robustness and the ability to generalize across time.
Compared with conventional iterative solvers, the method achieves similar reconstruction quality while reducing wall-clock time by more than two-fold, using Poisson negative log-likelihood as a convergence metric.
The approach has been integrated into an existing reconstruction pipeline and deployed in production at a synchrotron beamline for real-time experimental operation.

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.