# An Iterative Wavelet Threshold for Signal Denoising

arXiv: https://arxiv.org/abs/2307.10509 · HTML: https://mlsanity.com/paper/2307.10509/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2023.

## Built on by (most cited first, 3 of 3)

- [Wavelet-Based Image Tokenizer for Vision Transformers](https://mlsanity.com/paper/2405.18616/) (2024)
- [A Co-training Approach for Noisy Time Series Learning](https://mlsanity.com/paper/2308.12551/) (2023)
- [A self-supervised learning approach for denoising autoregressive models with additive noise: finite and infinite variance cases](https://mlsanity.com/paper/2508.12970/) (2025)

## Abstract (arXiv)

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.