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.

翻译：叠层衍射成像是一种扫描相干衍射成像技术，能够对扩展样品中的纳米级特征进行成像。其主要挑战在于，广泛使用的迭代图像重建方法通常要求相邻扫描位置之间存在显著重叠，导致数据量大、采集时间长。为克服这一关键限制，本文提出一种适用于叠层衍射成像的贝叶斯反演方法，该方法即使在相邻扫描位置重叠较少的情况下仍能有效工作。此外，所提方法能够量化叠层衍射物体固有的不确定性，这种不确定性源于叠层衍射反问题的不适定性。从高层次看，所提方法首先利用深度生成模型学习物体的先验分布，随后通过马尔可夫链蒙特卡洛算法从物体的后验分布中生成样本。我们在模拟叠层衍射实验中的结果表明，在重叠减少的情况下，所提框架能够持续优于广泛使用的迭代重建算法。此外，所提框架能够提供与真实误差紧密相关的不确定性估计，而这在实际应用中通常无法获得。项目网站可在此处访问。