标题： 利用小波、轮廓波和脊波进行日冕物质抛射检测：空间天气监测的应用 显示英文标题

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

摘要： 日冕物质抛射（CMEs）是等离子体和磁场的大规模喷发，可能在地球和其他太阳系区域产生不利的空间天气。 由于日冕仪图像中特征的固有多尺度性质，小波和多尺度图像处理技术非常适合增强CME的可见性并抑制噪声。 然而，小波更适合于识别点状特征，如噪声或背景恒星，而不是增强典型CME前缘的弯曲形态的可见性。 因此，探索了更高阶的多尺度技术，如脊波和曲线波，以表征CME的形态（宽度、曲率）和运动学（位置、速度、加速度）。 特别是曲线波被发现能够以自洽的方式表征CME的特性。 因此，曲线波可能有助于空间天气应用中的CME特性的自主监测。 显示英文摘要

摘要： 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.