Nonlinear noise suppression using a parametric class of wavelet shrinkage functions

Christopher B. Smith; Sos S. Agaian

doi:10.1117/12.542103

12 April 2004 Nonlinear noise suppression using a parametric class of wavelet shrinkage functions

Christopher B. Smith, Sos S. Agaian

Proceedings Volume 5439, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks II; (2004) https://doi.org/10.1117/12.542103
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

Donoho developed nonlinear techniques known as wavelet shrinkage. They have since been successfully applied for noise suppression. This paper introduces a new parametric shrinkage technique and compares its performance to the soft threshold introduced by Donoho and the differentiable shrinkage function introduced by Zhang. Termed the polynomial hard threshold this new shrinkage technique is better able to represent polynomial behavior than the previous techniques. It is also able to represent a wider class of shrinkage functions making it ideal for use in adaptive noise suppression. This class of shrinkage functions includes both Donoho’s soft and the classical hard threshold. By using a priori knowledge to adjust its parameters this threshold can be tailored to perform well for a particular signal type.

Citation Download Citation

Christopher B. Smith and Sos S. Agaian "Nonlinear noise suppression using a parametric class of wavelet shrinkage functions", Proc. SPIE 5439, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks II, (12 April 2004); https://doi.org/10.1117/12.542103

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