Visualizing dispersive features in 2D image via minimum gradient method

TL;DR

This paper introduces a minimum gradient method for visualizing dispersive features in 2D images, offering improved noise resilience and contrast, and demonstrates its application to spectroscopy data.

Contribution

A novel minimum gradient based approach for tracking ridge features in 2D images, outperforming existing methods in noise resilience and contrast preservation.

Findings

Enhanced detection of weak intensity features

Good noise resilience demonstrated through simulations

Successful application to spectroscopy measurements

Abstract

We developed a minimum gradient based method to track ridge features in 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, 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.