# Deep Iterative Phase Retrieval for Ptychography

arXiv: https://arxiv.org/abs/2202.10573 · HTML: https://mlsanity.com/paper/2202.10573/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [prDeep: Robust Phase Retrieval with a Flexible Deep Network](https://mlsanity.com/paper/1803.00212/) (2018)
- [Deep Iterative Reconstruction for Phase Retrieval](https://mlsanity.com/paper/1904.11301/) (2019)
- [Deep Ptych: Subsampled Fourier Ptychography using Generative Priors](https://mlsanity.com/paper/1812.11065/) (2018)
- [Deep Griffin-Lim Iteration](https://mlsanity.com/paper/1903.03971/) (2019)
- [Fast Griffin Lim based Waveform Generation Strategy for Text-to-Speech Synthesis](https://mlsanity.com/paper/2007.05764/) (2020)

## Built on by (most cited first, 3 of 3)

- [PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction](https://mlsanity.com/paper/2310.07504/) (2023)
- [Towards generalizable deep ptychography neural networks](https://mlsanity.com/paper/2509.25104/) (2025)
- [Circular Phase Representation and Geometry-Aware Optimization for Ptychographic Image Reconstruction](https://mlsanity.com/paper/2604.26664/) (2026)

## Abstract (arXiv)

One of the most prominent challenges in the field of diffractive imaging is the phase retrieval (PR) problem: In order to reconstruct an object from its diffraction pattern, the inverse Fourier transform must be computed. This is only possible given the full complex-valued diffraction data, i.e. magnitude and phase. However, in diffractive imaging, generally only magnitudes can be directly measured while the phase needs to be estimated. In this work we specifically consider ptychography, a sub-field of diffractive imaging, where objects are reconstructed from multiple overlapping diffraction images. We propose an augmentation of existing iterative phase retrieval algorithms with a neural network designed for refining the result of each iteration. For this purpose we adapt and extend a recently proposed architecture from the speech processing field. Evaluation results show the proposed approach delivers improved convergence rates in terms of both iteration count and algorithm runtime.