We present eMAGPIE (extended Multilevel-Adaptive-Guided Ptychographic Iterative Engine), a stochastic multigrid method for blind ptychographic phase retrieval that jointly recovers the object and the probe. We recast the task as the iterative minimization of a quadratic surrogate that majorizes the exit-wave misfit. From this surrogate, we derive closed-form updates, combined in a geometric-mean, phase-aligned joint step, yielding a simultaneous update of the object and probe with guaranteed descent of the sampled surrogate. This formulation naturally admits a multigrid acceleration that speeds up convergence. In experiments, eMAGPIE attains lower data misfit and phase error at comparable compute budgets and produces smoother, artifact-reduced phase reconstructions.
@article{arxiv.2511.01793,
title = {Stochastic Multigrid Method for Blind Ptychographic Phase Retrieval},
author = {Borong Zhang and Junjing Deng and Yi Jiang and Zichao Wendy Di},
journal= {arXiv preprint arXiv:2511.01793},
year = {2025}
}