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).

Fault Line Selection of Power System Based on Dual-Tree Complex Wavelet

Through analysis of various concept methods, developing route selection device based on double tree complex wavelet and transient energy, use the method based on transient component can improve line selection accuracy effectively. sensitivity and directional selectivity can improved by using dual tree complex wavelet transform than using discrete wavelet translation, the method can’t infect by the line of power system and the condition of the fault line. The simulation results show that by using the dual tree complex wavelet analysis in suitable scale can select the fault line correctly.