Abstract
In order to identify more new supernova remnants, astronomers are concerned about extracting useful signals from the observed data interspersed with various noises. The wavelet analysis is a newly developed time-frequency analysis method. Like Fourier analysis, any function with finite energy can be expanded on the orthonormal wavelet bases. Unlike Fourier bases, wavelet bases have the property of compact support. This good property makes the wavelet method a very effective way for separating the local noises from the observed data. The authors have succeeded in rejecting the interfering lines existing in the observed radio maps by using Daubechies 4 wavelet. The result clearly shows that wavelet method can retain useful information as much as possible during denosing.
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