Ptychography is a powerful imaging technique that is used in a variety of fields, including materials science, biology, and nanotechnology. However, the accuracy of the reconstructed ptychography image is highly dependent on the accuracy of the recorded probe positions which often contain errors. These errors are typically corrected jointly with phase retrieval through numerical optimization approaches. When the error accumulates along the scan path or when the error magnitude is large, these approaches may not converge with satisfactory result. We propose a fundamentally new approach for ptychography probe position prediction for data with large position errors, where a neural network is used to make single-shot phase retrieval on individual diffraction patterns, yielding the object image at each scan point. The pairwise offsets among these images are then found using a robust image registration method, and the results are combined to yield the complete scan path by constructing and solving a linear equation. We show that our method can achieve good position prediction accuracy for data with large and accumulating errors on the order of $10^2$ pixels, a magnitude that often makes optimization-based algorithms fail to converge. For ptychography instruments without sophisticated position control equipment such as interferometers, our method is of significant practical potential.

翻译：叠层衍射成像是一种强大的成像技术，广泛应用于材料科学、生物学和纳米技术等领域。然而，重建的叠层衍射图像精度高度依赖于记录的探针位置精度，而该位置信息通常存在误差。这些误差通常通过数值优化方法与相位恢复过程联合校正。当误差沿扫描路径累积或误差幅度较大时，这些方法可能无法收敛到令人满意的结果。我们提出了一种针对存在大位置误差数据的叠层衍射探针位置预测的全新方法：利用神经网络对单个衍射图样进行单次相位恢复，获得每个扫描点的物体图像。随后通过稳健的图像配准方法计算这些图像间的成对偏移量，并通过构建和求解线性方程组将结果整合，从而得到完整的扫描路径。实验表明，对于存在高达$10^2$像素量级的大幅度累积误差数据，本方法仍能实现良好的位置预测精度，而该误差量级常导致基于优化的算法无法收敛。对于缺乏干涉仪等精密位置控制设备的叠层衍射仪器，本方法具有重要的实际应用潜力。