Machine learningDenoising

Wavelet Signal Denoising (Soft Thresholding)

Wavelet signal denoising, introduced by David Donoho in 1995, is a non-parametric technique that removes noise from one-dimensional or multidimensional signals by decomposing them into wavelet coefficients, suppressing small coefficients that likely represent noise via a soft-thresholding operator, and reconstructing a smooth estimate. It is widely used in biomedical signal processing, geophysics, audio engineering, and image analysis where the underlying signal is assumed to be sparse or piecewise smooth.

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Sources

  1. Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41(3), 613–627. DOI: 10.1109/18.382009

Related methods

ScholarGateSignal Denoising (Wavelet Signal Denoising (Soft Thresholding)). Retrieved 2026-06-04 from https://scholargate.app/en/signal-processing/signal-denoising