Abstract
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).
Highlights
A substantial part of interstellar medium exists in the form of a fascinating web of omnipresent filamentary structures [1], called filaments
We develop an improved method based on morphology components analysis (MCA) and getfilaments
We focus on the image decomposition into two components: cartoon layer and texture layer
Summary
A substantial part of interstellar medium exists in the form of a fascinating web of omnipresent filamentary structures [1], called filaments. Tugay [13] proposed a layer smoothing method, Advances in Astronomy which described cellular large-scale structure of the universe (LSS) as a grid of clusters with density larger than a limited value, to detect extragalactic filaments. Men’shchikov [14] proposed a multi-scale filaments extraction method named getfilaments, which decomposed a simulated astronomical image containing filaments into spatial images at different scales to prevent interaction influence of different spatial scale structures. The getfilaments works well in simulated images and has been used to identify filaments for real astronomical images, e.g., the far-infrared images of Musca cloud observed with Herschel [15]. We develop an improved method based on morphology components analysis (MCA) and getfilaments.
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