Integrating Generative and Physics-Based Models for Ptychographic Imaging with Uncertainty Quantification

TL;DR

This paper introduces a Bayesian inversion approach combining deep generative models and MCMC sampling for ptychography, enabling effective imaging with less overlap and providing uncertainty quantification, thus improving reconstruction quality and reliability.

Contribution

It presents a novel Bayesian framework for ptychography that reduces overlap requirements and quantifies uncertainty, outperforming traditional iterative methods.

Findings

Outperforms iterative algorithms with less overlap in simulated experiments.

Provides uncertainty estimates that closely match true errors.

Demonstrates effectiveness in reducing data acquisition time.

Abstract

Ptychography is a scanning coherent diffractive imaging technique that enables imaging nanometer-scale features in extended samples. One main challenge is that widely used iterative image reconstruction methods often require significant amount of overlap between adjacent scan locations, leading to large data volumes and prolonged acquisition times. To address this key limitation, this paper proposes a Bayesian inversion method for ptychography that performs effectively even with less overlap between neighboring scan locations. Furthermore, the proposed method can quantify the inherent uncertainty on the ptychographic object, which is created by the ill-posed nature of the ptychographic inverse problem. At a high level, the proposed method first utilizes a deep generative model to learn the prior distribution of the object and then generates samples from the posterior distribution of the object by using a Markov Chain Monte Carlo algorithm. Our results from simulated ptychography experiments show that the proposed framework can consistently outperform a widely used iterative reconstruction algorithm in cases of reduced overlap. Moreover, the proposed framework can provide uncertainty estimates that closely correlate with the true error, which is not available in practice. The project website is available here.