Naveed Khuram, Shaukat Bisma, Ur Rehman Naveed
Department of Electrical Engineering, COMSATS University Islamabad (CUI), Park Road, Islamabad, Pakistan.
R Soc Open Sci. 2018 Sep 19;5(9):180436. doi: 10.1098/rsos.180436. eCollection 2018 Sep.
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.
提出了一种新颖的信号去噪方法，该方法将拟合优度（GOF）检验与基于多数分类的邻域滤波相结合，应用于对噪声信号进行双树复数小波变换（DT-CWT）得到的复数小波系数。与传统离散小波变换（DWT）相比，DT-CWT由于其近似平移不变性，已被证明是一种更好的信号去噪工具。所提出的框架通过在DT-CWT系数上局部执行GOF检验以对其进行信号或噪声的初步分类/检测，从而利用统计邻域依赖性。接下来，采用基于多数噪声分类的确定性邻域滤波方法来检测被误分类为噪声的信号系数（通过GOF检验），随后对这些系数进行恢复。所提出的方法在信号去噪方面表现出与现有技术相竞争的性能。
