Abstract:
:A novel signal denoising method is proposed whereby goodness-of-fit (GOF) test in combination with a majority classifications-based neighbourhood filtering is employed on complex wavelet coefficients obtained by applying dual tree complex wavelet transform (DT-CWT) on a noisy signal. The DT-CWT has proven to be a better tool for signal denoising as compared to the conventional discrete wavelet transform (DWT) owing to its approximate translation invariance. The proposed framework exploits statistical neighbourhood dependencies by performing the GOF test locally on the DT-CWT coefficients for their preliminary classification/detection as signal or noise. Next, a deterministic neighbourhood filtering approach based on majority noise classifications is employed to detect false classification of signal coefficients as noise (via the GOF test) which are subsequently restored. The proposed method shows competitive performance against the state of the art in signal denoising.
journal_name
R Soc Open Scijournal_title
Royal Society open scienceauthors
Naveed K,Shaukat B,Ur Rehman Ndoi
10.1098/rsos.180436subject
Has Abstractpub_date
2018-09-19 00:00:00pages
180436issue
9issn
2054-5703pii
rsos180436journal_volume
5pub_type
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