论文标题
ptychography的深度生成方法
A Deep Generative Approach to Oversampling in Ptychography
论文作者
论文摘要
PtyChography是一种经过良好研究的相成像方法，可以使非侵入性成像在纳米尺度上成为可能。它已经发展成为一种主流技术，在材料科学或国防工业等各个领域具有各种应用。 PtyChography的一个主要缺点是由于相邻照明区域之间的高重叠要求以实现合理的重建，因此数据采集时间很长。传统方法在扫描区域之间重叠减少会导致与文物的重建。在本文中，我们提出了从深层生成网络中采样的数据，以满足PtyChography的过度采样要求，并提出补充稀疏获得或不足的数据。由于深度生成网络是预先训练的，并且可以在收集数据时计算其输出，因此可以减少实验数据和获取数据的时间。我们通过提出重建质量与先前提出的和传统方法相比，通过提出重建质量来验证该方法，并评论提出的方法的优势和缺点。
Ptychography is a well-studied phase imaging method that makes non-invasive imaging possible at a nanometer scale. It has developed into a mainstream technique with various applications across a range of areas such as material science or the defense industry. One major drawback of ptychography is the long data acquisition time due to the high overlap requirement between adjacent illumination areas to achieve a reasonable reconstruction. Traditional approaches with reduced overlap between scanning areas result in reconstructions with artifacts. In this paper, we propose complementing sparsely acquired or undersampled data with data sampled from a deep generative network to satisfy the oversampling requirement in ptychography. Because the deep generative network is pre-trained and its output can be computed as we collect data, the experimental data and the time to acquire the data can be reduced. We validate the method by presenting the reconstruction quality compared to the previously proposed and traditional approaches and comment on the strengths and drawbacks of the proposed approach.
