Joint ptycho-tomography with deep generative priors

TL;DR

This paper introduces a joint ptycho-tomography framework that integrates deep generative priors using an ADMM-based approach, enabling high-quality 3D reconstructions from limited and noisy data.

Contribution

It extends ADMM with plug-and-play denoisers and proposes a parameter tuning method for learned priors, improving reconstruction quality and efficiency.

Findings

High-quality reconstructions with limited data

Effective integration of learned priors

Accelerated convergence through denoiser parameter tuning

Abstract

Joint ptycho-tomography is a powerful computational imaging framework to recover the refractive properties of a 3D object while relaxing the requirements for probe overlap that is common in conventional phase retrieval. We use an augmented Lagrangian scheme for formulating the constrained optimization problem and employ an alternating direction method of multipliers (ADMM) for the joint solution. ADMM allows the problem to be split into smaller and computationally more efficient subproblems: ptychographic phase retrieval, tomographic reconstruction, and regularization of the solution. We extend our ADMM framework with plug-and-play (PnP) denoisers by replacing the regularization subproblem with a general denoising operator based on machine learning. While the PnP framework enables integrating such learned priors as denoising operators, tuning of the denoiser prior remains challenging. To overcome this challenge, we propose a denoiser parameter to control the effect of the denoiser and to accelerate the solution. In our simulations, we demonstrate that our proposed framework with parameter tuning and learned priors generates high-quality reconstructions under limited and noisy measurement data.