Stochastic Multigrid Method for Blind Ptychographic Phase Retrieval
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915593922281472 |
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| author | Zhang, Borong Deng, Junjing Jiang, Yi Di, Zichao Wendy |
| author_facet | Zhang, Borong Deng, Junjing Jiang, Yi Di, Zichao Wendy |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_01793 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stochastic Multigrid Method for Blind Ptychographic Phase Retrieval Zhang, Borong Deng, Junjing Jiang, Yi Di, Zichao Wendy Numerical Analysis Optimization and Control 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. |
| title | Stochastic Multigrid Method for Blind Ptychographic Phase Retrieval |
| topic | Numerical Analysis Optimization and Control |
| url | https://arxiv.org/abs/2511.01793 |