Wavelet denoising using principal component analysis

In this paper, we propose wavelet-based denoising method using principal component analysis, which generalizes the univariate denoising and combines with principal component analysis. Two synthetic data sets, originally designed by Donoho and Johnstone to isolate and mimic various features found in real signals, and their correlated versions corrupted with Gaussian noise are used to test this method and the results show that this method is appropriate to multivariate signal denoising.