基于形态学成分分析的射电天文图像细丝提取

IF 1.6 4区 物理与天体物理 Q3 ASTRONOMY & ASTROPHYSICS Advances in Astronomy Pub Date : 2019-06-02 DOI:10.1155/2019/2397536
M. Zhu, W. Liu, B. Y. Wang, M. Zhang, W. Tian, X. Yu, T. Liang, D. Wu, D. Hu, F. Duan
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引用次数: 0

摘要

细丝是一种广泛存在的天文结构。从射电天文图像中分离细丝是一项挑战,因为它们的辐射通常很弱。此外,细丝经常与明亮的物体(如恒星)混合,这使得很难将它们分离。为了提取细丝,A.Men’schikov提出了一种自动寻找细丝的方法“getfilames”。然而,该算法通过简单地计算连接像素的数量来去除微小的结构。基于局部信息去除微小结构可能会去除部分细丝,因为射电天文图像中的细丝通常很弱。为了解决这一问题,我们将形态分量分析(MCA)应用于处理每个单个空间尺度的图像,并提出了一种基于MCA的细丝提取算法。MCA使用一个字典来分解图像,该字典的元素可以是小波转换函数、曲线转换函数或脊波转换函数。字典中元素的不同选择可以得到空间尺度图像的不同形态成分。通过使用MCA,我们可以获得空间尺度图像中的线结构、高斯源和其他结构,并排除与细丝无关的成分。实验结果表明,我们提出的基于MCA的方法能够有效地从真实的射电天文图像中提取细丝,并且该方法处理的图像具有较高的峰值信噪比。
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Extracting Filaments Based on Morphology Components Analysis from Radio Astronomical Images
Filaments are a type of wide-existing astronomical structure. It is a challenge to separate filaments from radio astronomical images, because their radiation is usually weak. What is more, filaments often mix with bright objects, e.g., stars, which makes it difficult to separate them. In order to extract filaments, A. Men’shchikov proposed a method “getfilaments” to find filaments automatically. However, the algorithm removed tiny structures by counting connected pixels number simply. Removing tiny structures based on local information might remove some part of the filaments because filaments in radio astronomical image are usually weak. In order to solve this problem, we applied morphology components analysis (MCA) to process each singe spatial scale image and proposed a filaments extraction algorithm based on MCA. MCA uses a dictionary whose elements can be wavelet translation function, curvelet translation function, or ridgelet translation function to decompose images. Different selection of elements in the dictionary can get different morphology components of the spatial scale image. By using MCA, we can get line structure, gauss sources, and other structures in spatial scale images and exclude the components that are not related to filaments. Experimental results showed that our proposed method based on MCA is effective in extracting filaments from real radio astronomical images, and images processed by our method have higher peak signal-to-noise ratio (PSNR).
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来源期刊
Advances in Astronomy
Advances in Astronomy ASTRONOMY & ASTROPHYSICS-
CiteScore
2.70
自引率
7.10%
发文量
10
审稿时长
22 weeks
期刊介绍: Advances in Astronomy publishes articles in all areas of astronomy, astrophysics, and cosmology. The journal accepts both observational and theoretical investigations into celestial objects and the wider universe, as well as the reports of new methods and instrumentation for their study.
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