# Regularized Fourier Ptychography using an Online Plug-and-Play Algorithm

arXiv: https://arxiv.org/abs/1811.00120 · HTML: https://mlsanity.com/paper/1811.00120/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2018.

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

- [Online Deep Equilibrium Learning for Regularization by Denoising](https://mlsanity.com/paper/2205.13051/) (2022)
- [Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis](https://mlsanity.com/paper/2310.00133/) (2023)
- [Deformation-Compensated Learning for Image Reconstruction without Ground Truth](https://mlsanity.com/paper/2107.05533/) (2021)
- [Deep Model-Based Architectures for Inverse Problems under Mismatched Priors](https://mlsanity.com/paper/2207.13200/) (2022)
- [Tensorial tomographic differential phase-contrast microscopy](https://mlsanity.com/paper/2204.11397/) (2022)
- [Designing across domains with declarative thinking: Insights from the 96-Eyes ptychographic imager project](https://mlsanity.com/paper/2508.18512/) (2025)
- [Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction](https://mlsanity.com/paper/2605.01122/) (2026)

## Abstract (arXiv)

The plug-and-play priors (PnP) framework has been recently shown to achieve state-of-the-art results in regularized image reconstruction by leveraging a sophisticated denoiser within an iterative algorithm. In this paper, we propose a new online PnP algorithm for Fourier ptychographic microscopy (FPM) based on the fast iterative shrinkage/threshold algorithm (FISTA). Specifically, the proposed algorithm uses only a subset of measurements, which makes it scalable to a large set of measurements. We validate the algorithm by showing that it can lead to significant performance gains on both simulated and experimental data.