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Multisignal 1-D denoising using wavelets


[XD,DECDEN,THRESH] = mswden('den',...)
[XD,THRESH] = mswden('densig',...)
[DECDEN,THRESH] = mswden('dendec',...)
THRESH = mswden('thr',...)
[...] = mswden(...,S_OR_H)
[...] = mswden(...,S_OR_H,KEEPAPP)
[...] = mswden(...,S_OR_H,KEEPAPP,IDXSIG)


mswden computes thresholds and, depending on the selected option, performs denoising of 1-D signals using wavelets.

[XD,DECDEN,THRESH] = mswden('den',...) returns a denoised version XD of the original multisignal matrix X, whose wavelet decomposition structure is DEC. The output XD is obtained by thresholding the wavelet coefficients, DECDEN is the wavelet decomposition associated to XD (see mdwtdec), and THRESH is the matrix of threshold values. The input METH is the name of the denoising method and PARAM is the associated parameter, if required.

Valid denoising methods METH and associated parameters PARAM are:


Principle of Stein's Unbiased Risk


Heuristic variant of the first option


Universal threshold sqrt(2*log(.))


Minimax thresholding (see thselect)

For these methods PARAM defines the multiplicative threshold rescaling:


No rescaling


Rescaling using a single estimation of level noise based on first level coefficients


Rescaling using a level dependent estimation of level noise

Penalization methods




Penal high, 2.5 ℜ≤ PARAM ℜ≤ 10


Penal medium, 1.5 ℜ≤ PARAM ℜ≤ 2.5


Penal low, 1 ℜ≤ PARAM ℜ≤ 2

PARAM is a sparsity parameter, and it should be such that: 1PARAM10. For penal method, no control is done.

Manual method


Manual method

PARAM is an NbSIG-by-NbLEV matrix or NbSIG-by-(NbLEV+1) matrix such that:

  • PARAM(i,j) is the threshold for the detail coefficients of level j for the ith signal (1jNbLEV).

  • PARAM(i,NbLEV+1) is the threshold for the approximation coefficients for the ith signal (if KEEPAPP is 0).

where NbSIG is the number of signals and NbLEV the number of levels of decomposition.

Instead of the 'den' input OPTION, you can use 'densig', 'dendec' or 'thr' OPTION to select output arguments:

[XD,THRESH] = mswden('densig',...) or [DECDEN,THRESH] = mswden('dendec',...)

THRESH = mswden('thr',...) returns the computed thresholds, but denoising is not performed.

The decomposition structure input argument DEC can be replaced by four arguments: DIRDEC, X, WNAME and LEV.

[...] = mswden(OPTION,DIRDEC,X,WNAME,LEV,METH,PARAM) before performing a denoising or computing thresholds, the multisignal matrix X is decomposed at level LEV using the wavelet WNAME, in the direction DIRDEC.

You can use three more optional inputs:

[...] = mswden(...,S_OR_H) or
[...] = mswden(...,S_OR_H,KEEPAPP) or
[...] = mswden(...,S_OR_H,KEEPAPP,IDXSIG)

  • S_OR_H ('s' or 'h') stands for soft or hard thresholding (see mswthresh for more details).

  • KEEPAPP (true or false) indicates whether to keep approximation coefficients (true) or not (false).

  • IDXSIG is a vector that contains the indices of the initial signals, or the character vector 'all'.

The defaults are, respectively, 'h', false and 'all'.


% Load original 1D-multisignal.
load thinker

% Perform a decomposition at level 2 using the wavelet db2.
dec = mdwtdec('r',X,2,'db2');

% Denoise signals using the universal method
% of thresholding (sqtwolog) and the 'sln' 
% threshold rescaling (with a single estimation
% of level noise, based on first level coefficients).
[XD,decDEN,THRESH] = mswden('den',dec,'sqtwolog','sln');

% Plot the original signals 1 and 31, and the 
% corresponding denoised signals.
plot(X([1 31],:)','r--','linewidth',2);   hold on
plot(XD([1 31],:)','b','linewidth',2);
grid; set(gca,'Xlim',[1,96])
title('X dashed line and XD solid line')


Birgé, L.; P. Massart (1997), “From model selection to adaptive estimation,” in D. Pollard (ed), Festchrift for L. Le Cam, Springer, pp. 55–88.

DeVore, R.A.; B. Jawerth, B.J. Lucier (1992), “Image compression through wavelet transform coding,” IEEE Trans. on Inf. Theory, vol. 38, No 2, pp. 719–746.

Donoho, D.L. (1993), “Progress in wavelet analysis and WVD: a ten minute tour,” in Progress in wavelet analysis and applications, Y. Meyer, S. Roques, pp. 109–128. Frontières Ed.

Donoho, D.L.; I.M. Johnstone(1994), “Ideal spatial adaptation by wavelet shrinkage,” Biometrika, vol. 81, pp. 425–455.

Donoho, D.L.; I.M. Johnstone, G. Kerkyacharian, D. Picard (1995), “Wavelet shrinkage: asymptopia,” Jour. Roy. Stat. Soc.,series B, vol. 57 no. 2, pp. 301–369.

Donoho, D.L.; I.M. Johnstone, “Ideal de-noising in an orthonormal basis chosen from a library of bases,” C.R.A.S. Paris, t. 319, Ser. I, pp. 1317–1322.

Donoho, D.L. (1995), “De-noising by soft-thresholding,” IEEE Trans. on Inf. Theory, 41, 3, pp. 613–627.

Introduced in R2007a

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