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

TL;DR

This paper introduces a physics-guided score-based diffusion model for ptychographic image reconstruction, significantly reducing data requirements and overlap ratios needed for high-quality results.

Contribution

It presents a novel diffusion model approach that incorporates physics-based data consistency, enabling high-fidelity reconstructions from limited data with minimal overlap.

Findings

Achieves high-quality reconstructions with only 20% overlap.

Outperforms traditional methods requiring 62% overlap.

Reduces data acquisition time and volume significantly.

Abstract

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.