# Ptychographic Image Reconstruction from Limited Data via Score-Based Diffusion Models with Physics-Guidance

arXiv: https://arxiv.org/abs/2502.18767 · HTML: https://mlsanity.com/paper/2502.18767/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Diffusion Posterior Sampling for General Noisy Inverse Problems](https://mlsanity.com/paper/2209.14687/) (2022)
- [FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model](https://mlsanity.com/paper/2303.09833/) (2023)
- [Phase Retrieval via Wirtinger Flow: Theory and Algorithms](https://mlsanity.com/paper/1407.1065/) (2014)
- [Deep Learning for Photoacoustic Tomography from Sparse Data](https://mlsanity.com/paper/1704.04587/) (2017)
- [Deep learning at the edge enables real-time streaming ptychographic imaging](https://mlsanity.com/paper/2209.09408/) (2022)
- [PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction](https://mlsanity.com/paper/2310.07504/) (2023)
- [Accelerated Wirtinger Flow: A fast algorithm for ptychography](https://mlsanity.com/paper/1806.05546/) (2018)
- [Scalable and accurate multi-GPU based image reconstruction of large-scale ptychography data](https://mlsanity.com/paper/2106.07575/) (2021)
- [Compressive Ptychography using Deep Image and Generative Priors](https://mlsanity.com/paper/2205.02397/) (2022)
- [Projected Multi-Agent Consensus Equilibrium (PMACE) with Application to Ptychography](https://mlsanity.com/paper/2303.15679/) (2023)
- [AI-assisted Automated Workflow for Real-time X-ray Ptychography Data Analysis via Federated Resources](https://mlsanity.com/paper/2304.04297/) (2023)
- [Integrating Generative and Physics-Based Models for Ptychographic Imaging with Uncertainty Quantification](https://mlsanity.com/paper/2412.10882/) (2024)

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

- [Improving Multislice Electron Ptychography with a Generative Prior](https://mlsanity.com/paper/2507.17800/) (2025)
- [Towards generalizable deep ptychography neural networks](https://mlsanity.com/paper/2509.25104/) (2025)
- [Contrast-invariant deep ptychography neural networks](https://mlsanity.com/paper/2608.02869/) (2026)

## Abstract (arXiv)

Ptychography is a data-intensive computational imaging technique that achieves high spatial resolution over large fields of view. The technique involves scanning a coherent beam across overlapping regions and recording diffraction patterns. Conventional reconstruction algorithms require substantial overlap, increasing data volume and experimental time, reaching PiB-scale experimental data and weeks to month-long data acquisition times. To address this, we propose a reconstruction method employing a physics-guided score-based diffusion model. Our approach trains a diffusion model on representative object images to learn an object distribution prior. During reconstruction, we modify the reverse diffusion process to enforce data consistency, guiding reverse diffusion toward a physically plausible solution. This method requires a single pretraining phase, allowing it to generalize across varying scan overlap ratios and positions. Our results demonstrate that the proposed method achieves high-fidelity reconstructions with only a 20% overlap, while the widely employed rPIE method requires a 62% overlap to achieve similar accuracy. This represents a significant reduction in data requirements, offering an alternative to conventional techniques.