用生成模型与物理模型结合，减少扫描重叠仍能高精度成像并量化误差。
Integrating Generative and Physics-Based Models for Ptychographic Imaging with Uncertainty Quantification
- 用深度生成模型学习物体先验，结合马尔可夫链蒙特卡洛采样推后验分布。
- 在低重叠条件下重建精度优于传统迭代算法，且误差估计与真实误差高度相关。
- 适合需要可靠成像质量评估的纳米成像研究者，尤其关注不确定性建模者。
Ptychography 是一种扫描相干衍射成像技术，可实现对大尺度样品的纳米级特征成像。其主要挑战在于，广泛使用的迭代重建方法通常需要相邻扫描位置间有较大重叠，导致数据量庞大和采集时间延长。本文提出一种贝叶斯反演方法，即使在邻近扫描位置重叠较少的情况下也能有效工作。此外，该方法可量化由反问题病态性引起的物体不确定性。总体思路是：首先利用深度生成模型学习物体的先验分布，然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。模拟实验结果表明，所提框架在重叠减少的情况下，始终优于广泛应用的迭代重建算法；同时，其提供的不确定性估计与真实误差高度相关，而这是以往方法无法实现的。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。