Hysteresis Thresholding: A Graph-Based Wavelet Block Denoising Algorithm

Radu Ranta*, Valérie Louis-Dorr
Centre de Recherche en Automatique de Nancy (CRAN), Nancy-Université, CNRS, 2 avenue de la Forêt de Haye F-54516 Vandoeuvre-les-Nancy, France

© Ranta et al.;

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Centre de Recherche en Automatique de Nancy (CRAN), Nancy-Universite, CNRS, 2 avenue de la Foret de Haye F-54516 Vandoeuvre-les-Nancy, France.


This communication aims to combine several previously proposed wavelet denoising algorithms into a novel heuristic block method. The proposed “hysteresis” thresholding uses two thresholds simultaneously in order to combine detection and minimal alteration of informative features of the processed signal. This approach exploits the graph structure of the wavelet decomposition to detect clusters of significant wavelet coefficients. The new algorithm is compared with classical denoising methods on simulated benchmark signals.

Keywords: Wavelets, block denoising, graphs, transitive closure.