论文标题
用于光谱分析的边缘检测和图像滤波器算法和深度学习应用
Edge Detection and Image Filter algorithms for Spectroscopic Analysis with Deep Learning Applications
论文作者
论文摘要
边缘检测和图像过滤器通常用于计算机视觉中。但是，在以系统的方式之前，它们从未应用于角度分辨光发射光谱（ARPES）数据的数据分析。在本文中，我们将使用HFP2，ZRSIS和HF2TE2P2的ARPES结果中使用高斯（log），Canny，PreWitt，Roberts和模糊逻辑方法的Sobel，Laplacian，Canny，PreWitt，Roberts和Fuzzy Logic方法。我们发现，Canny过滤器是典型的ARPES测量的噪声数据的边缘检测的最佳方法，而另一个边缘检测技术无法正确检测ARPES频段。
Edge detection and image filters are commonly used in computer vision. However, they have never been applied to the data analysis of angle-resolved photoemission spectroscopy (ARPES) data before in a systematic fashion. In this paper we will use the Sobel, Laplacian of a gaussian (LoG), Canny, Prewitt, Roberts, and fuzzy logic methods for edge detection in the ARPES results of HfP2, ZrSiS, and Hf2Te2P2. We find that the Canny filter is the best method for edge detection of noisy data that is typical of ARPES measurements, while the other edge detection techniques are not able to correctly detect ARPES bands.
