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.本文提出一种新型信号去噪方法：对含噪信号实施双树复小波变换（dual tree complex wavelet transform, DTCWT）以获取复小波系数，随后结合拟合优度检验（goodness of fit, GOF）与基于多数分类的邻域滤波方法对其开展处理。相较于传统离散小波变换（discrete wavelet transform, DWT），双树复小波变换因具备近似平移不变性，已被证实为更优异的信号去噪工具。所提框架通过对双树复小波系数局部执行拟合优度检验，将其初步划分为信号或噪声分量，以此挖掘其统计邻域相关性；随后采用基于多数噪声分类结果的确定性邻域滤波方法，检测并修正被拟合优度检验误判为噪声的信号系数并予以恢复。相较于当前先进的信号去噪方法，所提方法展现出极具竞争力的性能表现。