日冕暗化图像检测算法的并行设计与实现

摘要

日冕物质抛射(Coronal mass ejection,CM E)是空间灾害天气最主要的驱动源.在CM E的众多伴生现象中,日冕暗化与之最为紧密相关.因此,对日冕暗化现象进行检测,有助于预报CM E的爆发.随着观测数据量的增加,现有日冕暗化检测算法的效率亟待提升.本文基于一种半自动暗化检测算法,提出并实现该算法的并行方案,以提高算法效率,为实现日冕暗化的实时检测奠定基础.首先介绍了日冕暗化的现有工作,接着详细分析了一种半自动暗化检测方法,它在一定程度上提高了人工识别暗化区域的效率,但其效率并不能满足检测的实时性要求.然后,基于M atlab R2014a平台并行机制的特点,从数据、计算量和图像分块角度对半自动暗化检测算法提出了3种不同的并行方案,实验对比分析结果表明图像分块并行方案的效果最优.%Coronal mass ejection (CME) is considered to be the main driving force of space weather disas-ters .CME often appears with many other solar activities .Since coronal dimming is associated most closely with CME ,the detection of coronal dimming can help to forecast CME .Continuous development of CME observations brings increasingly large amount of data ,whereas the efficiency of the detection al-gorithms of coronal dimming needs to be improved .Three parallel algorithms are presented for a semi-automatic dimming detection algorithm .They make the foundation for real-time detection of coronal dim-ming .We firstly introduce the existing work about the topic ,then analyze a semi-automatic dimming de-tection algorithm ,which to some degree ,improves the efficiency of artificial recognition of darkened are-a ,but does not meet the requirements of real-time detection .Based on the principle of parallelism of Matlab R2014a ,three different parallel algorithms are presented from different aspects ,i .e .data ,dis-tance-calculation and image-divide .Experimental results show that the parallelization based on image-di-vide is the most efficient one among the three algorithms .