# Deep Ptych: Subsampled Fourier Ptychography using Generative Priors

arXiv: https://arxiv.org/abs/1812.11065 · HTML: https://mlsanity.com/paper/1812.11065/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2018.

## Built on by (most cited first, 7 of 7)

- [On the use of deep learning for phase recovery](https://mlsanity.com/paper/2308.00942/) (2023)
- [First-Place Solution to NeurIPS 2024 Invisible Watermark Removal Challenge](https://mlsanity.com/paper/2508.21072/) (2025)
- [Makeup-Guided Facial Privacy Protection via Untrained Neural Network Priors](https://mlsanity.com/paper/2408.12387/) (2024)
- [Generative adversarial networks with physical sound field priors](https://mlsanity.com/paper/2308.00426/) (2023)
- [Deep Iterative Phase Retrieval for Ptychography](https://mlsanity.com/paper/2202.10573/) (2022)
- [Improving Multislice Electron Ptychography with a Generative Prior](https://mlsanity.com/paper/2507.17800/) (2025)
- [Position-Blind Ptychography: Viability of image reconstruction via data-driven variational inference](https://mlsanity.com/paper/2509.25269/) (2025)

## Abstract (arXiv)

This paper proposes a novel framework to regularize the highly ill-posed and non-linear Fourier ptychography problem using generative models. We demonstrate experimentally that our proposed algorithm, Deep Ptych, outperforms the existing Fourier ptychography techniques, in terms of quality of reconstruction and robustness against noise, using far fewer samples. We further modify the proposed approach to allow the generative model to explore solutions outside the range, leading to improved performance.