Coronal Mass Ejection Detection using Wavelets, Curvelets and Ridgelets: Applications for Space Weather Monitoring

TL;DR

This paper explores advanced multiscale image processing techniques, including wavelets, curvelets, and ridgelets, to improve the detection and characterization of coronal mass ejections for space weather monitoring.

Contribution

It introduces the use of higher order multiscale techniques, especially curvelets, for better morphological and kinematic analysis of CMEs in coronagraph images.

Findings

Curvelets effectively characterize CME morphology and motion.

Wavelets are less suited for curved CME features.

Curvelets can enhance autonomous space weather monitoring.

Abstract

Coronal mass ejections (CMEs) are large-scale eruptions of plasma and magnetic feld that can produce adverse space weather at Earth and other locations in the Heliosphere. Due to the intrinsic multiscale nature of features in coronagraph images, wavelet and multiscale image processing techniques are well suited to enhancing the visibility of CMEs and supressing noise. However, wavelets are better suited to identifying point-like features, such as noise or background stars, than to enhancing the visibility of the curved form of a typical CME front. Higher order multiscale techniques, such as ridgelets and curvelets, were therefore explored to characterise the morphology (width, curvature) and kinematics (position, velocity, acceleration) of CMEs. Curvelets in particular were found to be well suited to characterising CME properties in a self-consistent manner. Curvelets are thus likely to be of benefit to autonomous monitoring of CME properties for space weather applications.