An Iterative Wavelet Threshold for Signal Denoising

TL;DR

This paper presents SpcShrink, an adaptive wavelet thresholding method for signal denoising that uses an iterative algorithm inspired by statistical process control, demonstrating superior performance on biomedical data.

Contribution

Introduces SpcShrink, a novel iterative wavelet thresholding algorithm for signal denoising based on SPC principles, with demonstrated effectiveness on real biomedical data.

Findings

SpcShrink outperforms competing algorithms in denoising tasks.

The method effectively discriminates significant wavelet coefficients.

Numerical evaluations confirm the robustness of the approach.

Abstract

This paper introduces an adaptive filtering process based on shrinking wavelet coefficients from the corresponding signal wavelet representation. The filtering procedure considers a threshold method determined by an iterative algorithm inspired by the control charts application, which is a tool of the statistical process control (SPC). The proposed method, called SpcShrink, is able to discriminate wavelet coefficients that significantly represent the signal of interest. The SpcShrink is algorithmically presented and numerically evaluated according to Monte Carlo simulations. Two empirical applications to real biomedical data filtering are also included and discussed. The SpcShrink shows superior performance when compared with competing algorithms.