Data from: Dual tree complex wavelet transform based signal denoising method exploiting neighbourhood dependencies and goodness of fit test

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 (DTCWT) 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.DTCWT-GOF-NeighFiltData related to my article titled as "Dual tree complex wavelet transform based signal denoising using Neighbourhood filtering and goodness of fit test". This file contains Matlab signal denoising software and all the signals used in this study