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.

联合 ptycho-tomography 是一种强大的计算成像框架，可以恢复 3D 对象的折射特性，同时放宽传统相位检索中常见的探头重叠要求。我们使用增广拉格朗日方案来制定约束优化问题，并采用乘法器的交替方向方法 (ADMM) 进行联合求解。ADMM 允许将问题分解为更小且计算效率更高的子问题：ptychographic 相位检索、断层扫描重建和解决方案的正则化。我们通过使用基于机器学习的通用去噪算子替换正则化子问题，使用即插即用 (PnP) 去噪器扩展了我们的 ADMM 框架。虽然 PnP 框架能够集成诸如去噪算子之类的学习先验，降噪器先验的调整仍然具有挑战性。为了克服这一挑战，我们提出了一个降噪器参数来控制降噪器的效果并加速解决方案。在我们的模拟中，我们证明了我们提出的具有参数调整和学习先验的框架可以在有限和嘈杂的测量数据下生成高质量的重建。