Visualizing dispersive features in 2D image via minimum gradient method

被引：16

作者：

He, Yu ^{[1
,2
]}

Wang, Yan ^{[3
]}

Shen, Zhi-Xun ^{[1
,2
]}

机构：

[1] SLAC Natl Accelerator Lab, SIMES, Menlo Pk, CA 94025 USA

[2] Stanford Univ, Dept Appl Phys, Stanford, CA 94305 USA

[3] Microsoft Corp, Redmond, WA 98052 USA

来源：

REVIEW OF SCIENTIFIC INSTRUMENTS | 2017年 / 88卷 / 07期

关键词：

Image enhancement - Gradient methods - Photoelectron spectroscopy - Numerical methods;

D O I：

10.1063/1.4993919

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

We developed a minimum gradient based method to track ridge features in a 2D image plot, which is a typical data representation in many momentum resolved spectroscopy experiments. Through both analytic formulation and numerical simulation, we compare this new method with existing DC (distribution curve) based and higher order derivative based analyses. We find that the new method has good noise resilience and enhanced contrast especially for weak intensity features and meanwhile preserves the quantitative local maxima information from the raw image. An algorithm is proposed to extract 1D ridge dispersion from the 2D image plot, whose quantitative application to angle-resolved photoemission spectroscopy measurements on high temperature superconductors is demonstrated. Published by AIP Publishing.

引用

页数：9

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