# Compressive Ptychography using Deep Image and Generative Priors

arXiv: https://arxiv.org/abs/2205.02397 · HTML: https://mlsanity.com/paper/2205.02397/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 53 references that are in the map.
- First posted on arXiv 5 May 2022.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation](https://mlsanity.com/paper/2003.13659/) (2020)
- [Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging](https://mlsanity.com/paper/2203.17061/) (2022)
- [Lensless computational imaging through deep learning](https://mlsanity.com/paper/1702.08516/) (2017)
- [Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networks](https://mlsanity.com/paper/1810.03856/) (2018)
- [Reliable deep-learning-based phase imaging with uncertainty quantification](https://mlsanity.com/paper/1901.02038/) (2019)
- [Low Photon Count Phase Retrieval Using Deep Learning](https://mlsanity.com/paper/1806.10029/) (2018)
- [Real-time coherent diffraction inversion using deep generative networks](https://mlsanity.com/paper/1806.03992/) (2018)
- [Diffraction tomography with a deep image prior](https://mlsanity.com/paper/1912.05330/) (2019)
- [Joint ptycho-tomography with deep generative priors](https://mlsanity.com/paper/2009.09498/) (2020)
- [Robust Compressive Phase Retrieval via Deep Generative Priors](https://mlsanity.com/paper/1808.05854/) (2018)
- [Three dimensions, two microscopes, one code: automatic differentiation for x-ray nanotomography beyond the depth of focus limit](https://mlsanity.com/paper/1905.10433/) (2019)
- [A parameter refinement method for Ptychography based on Deep Learning concepts](https://mlsanity.com/paper/2105.08058/) (2021)
- [Projected Multi-Agent Consensus Equilibrium for Ptychographic Image Reconstruction](https://mlsanity.com/paper/2111.14240/) (2021)

## Built on by (most cited first, 5 of 5)

- [PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction](https://mlsanity.com/paper/2310.07504/) (2023)
- [Ptychographic Image Reconstruction from Limited Data via Score-Based Diffusion Models with Physics-Guidance](https://mlsanity.com/paper/2502.18767/) (2025)
- [Integrating Generative and Physics-Based Models for Ptychographic Imaging with Uncertainty Quantification](https://mlsanity.com/paper/2412.10882/) (2024)
- [Towards generalizable deep ptychography neural networks](https://mlsanity.com/paper/2509.25104/) (2025)
- [Autonomous Algorithm Discovery for Ptychography via Evolutionary LLM Reasoning](https://mlsanity.com/paper/2603.05696/) (2026)

## Abstract (arXiv)

Ptychography is a well-established coherent diffraction imaging technique that enables non-invasive imaging of samples at a nanometer scale. It has been extensively used in various areas such as the defense industry or materials science. One major limitation of ptychography is the long data acquisition time due to mechanical scanning of the sample; therefore, approaches to reduce the scan points are highly desired. However, reconstructions with less number of scan points lead to imaging artifacts and significant distortions, hindering a quantitative evaluation of the results. To address this bottleneck, we propose a generative model combining deep image priors with deep generative priors. The self-training approach optimizes the deep generative neural network to create a solution for a given dataset. We complement our approach with a prior acquired from a previously trained discriminator network to avoid a possible divergence from the desired output caused by the noise in the measurements. We also suggest using the total variation as a complementary before combat artifacts due to measurement noise. We analyze our approach with numerical experiments through different probe overlap percentages and varying noise levels. We also demonstrate improved reconstruction accuracy compared to the state-of-the-art method and discuss the advantages and disadvantages of our approach.