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BNU - Beijing Normal University
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Abstract Reference: 31035
Identifier: P2.27
Presentation: Poster presentation
Key Theme: 2 Management of Scientific and Data Analysis Projects

Extracting Filaments Based on Morphology Components Analysis from Radio Astronomical Images

Authors:
Wang Bingyi, Zhang Mengfei, Liang Tianheng, Wu Dan, Tian Wenwu, Duan Fuqing, Xianchuan Yu

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 and filaments often mix with bright objects, e.g. stars, which leads difficulty to separate them. In 2013, A. Men’ shchikov proposed a multi-scale, multi-wavelength filament extraction method, which decomposes a simulated astronomical image containing filaments into spatial scale images to prevent interaction influence of different spatial scale structures. However, the algorithm of processing each single spatial scale image in the method is used to simply remove tiny structures by counting connected pixels number. Removing tiny structures based on local information might remove some part of the filaments because filaments in real astronomic image are usually weak. We attempt to use Morphology Components Analysis (MCA) to process each singe spatial scale image. 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. Our experiments show that our method is efficient in filaments extraction from real radio astronomic images, and images processed by our method have higher PSNR (Peak Signal to Noise Ratio).