Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction
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 re...
| Authors | Bowen Zheng, Katayun Kamdin, David Shapiro et al. |
|---|---|
| Primary category | cs.LG |
| Published | 1 May 2026 |
| Last updated at source | 26 June 2026 |
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