Extracting Filaments Based on Morphology Components Analysis from Radio Astronomical Images
| Autor: | B. Y. Wang, Wei Liu, Zhu Meng, F. Q. Duan, M. F. Zhang, W. W. Tian, Xianchuan Yu, D. Hu, D. Wu, T. H. Liang |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Physics
Translation function Pixel Article Subject business.industry lcsh:Astronomy 0211 other engineering and technologies Process (computing) Extraction algorithm Astronomy and Astrophysics Pattern recognition 02 engineering and technology Image (mathematics) lcsh:QB1-991 Stars Wavelet Space and Planetary Science Computer Science::Computer Vision and Pattern Recognition 0202 electrical engineering electronic engineering information engineering Curvelet 020201 artificial intelligence & image processing Artificial intelligence business 021101 geological & geomatics engineering |
| Zdroj: | Advances in Astronomy, Vol 2019 (2019) |
| ISSN: | 1687-7969 |
| DOI: | 10.1155/2019/2397536 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
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