论文标题
物理启发的无监督分类X射线Ptychography领域
Physics-Inspired Unsupervised Classification for Region of Interest in X-Ray Ptychography
论文作者
论文摘要
X射线Ptychography允许由于大量数据而以额外的计算费用成像以高分辨率成像。鉴于有关对象的信息有限，获得的数据通常具有超出关注区域（ROI）之外的大量信息。在这项工作中，我们提出了一种由物理启发的无监督学习算法，以识别对象的ROI，仅在将计算资源进行重新构造之前，仅使用Ptychography数据集中的衍射模式来识别对象的ROI。从ROI内部自动识别为不在ROI内的衍射模式被过滤掉，从而通过仅关注ROI内的重要数据，同时保留图像质量，从而可以有效地重建。
X-ray ptychography allows for large fields to be imaged at high resolution at the cost of additional computational expense due to the large volume of data. Given limited information regarding the object, the acquired data often has an excessive amount of information that is outside the region of interest (RoI). In this work we propose a physics-inspired unsupervised learning algorithm to identify the RoI of an object using only diffraction patterns from a ptychography dataset before committing computational resources to reconstruction. Obtained diffraction patterns that are automatically identified as not within the RoI are filtered out, allowing efficient reconstruction by focusing only on important data within the RoI while preserving image quality.
