mswden

(Not recommended) Multisignal 1-D denoising using wavelets

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    mswden is not recommended. Use wdenoise instead.

    Syntax

    [xd,decden,thresh] = mswden("den",dec,meth,param)
    [xd,thresh] = mswden("densig",dec,meth,param)
    [decden,thresh] = mswden("dendec",dec,meth,param)
    thresh = mswden("thr",dec,meth,param)
    [___] = mswden(option,dirdec,x,wname,lev,meth,param)
    [___] = mswden(___,sorh)
    [___] = mswden(___,sorh,keepapp)
    [___] = mswden(___,sorh,keepapp,idxsig)

    Description

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

    [xd,decden,thresh] = mswden("den",dec,meth,param) returns a denoised version xd of the multisignal x whose wavelet decomposition is dec. meth is the name of the denoising method and param is the associated parameter, if required. decden is the wavelet decomposition of the denoised signal, and thresh are the threshold values.

    example

    [xd,thresh] = mswden("densig",dec,meth,param) returns a denoised version xd of the multisignal and the threshold values thresh.

    [decden,thresh] = mswden("dendec",dec,meth,param) returns the wavelet decomposition decden of the denoised multisignal and the threshold values thresh.

    thresh = mswden("thr",dec,meth,param) returns only the threshold values thresh. Denoising is not performed.

    [___] = mswden(option,dirdec,x,wname,lev,meth,param) before performing a denoising or computing thresholds, decomposes the multisignal x at level lev using the wavelet wname, in the direction dirdec. The mswden function returns the output arguments specified by option.

    [___] = mswden(___,sorh) specifies the type of thresholding to perform.

    [___] = mswden(___,sorh,keepapp) specifies whether to keep the approximation coefficients.

    [___] = mswden(___,sorh,keepapp,idxsig) specifies the indices of the initial signals, or "all".

    Examples

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    Open Live Script

    Load the 23 channel EEG data Espiga3 [8]. The channels are arranged column-wise. The data is sampled at 200 Hz.

    load Espiga3

    Perform a decomposition at level 2 using the db2 wavelet.

    dec = mdwtdec("c",Espiga3,2,"db2")
    dec = struct with fields:
        dirDec: 'c'
         level: 2
         wname: 'db2'
    dwtFilters: [1×1 struct]
       dwtEXTM: 'sym'
      dwtShift: 0
      dataSize: [995 23]
            ca: [251×23 double]
            cd: {[499×23 double]  [251×23 double]}

    Denoise the signals using the universal method of thresholding (sqtwolog) and the sln threshold rescaling (with a single estimation of level noise, based on the first level coefficients).

    [xd,decden,thresh] = mswden("den",dec,"sqtwolog","sln");

    Plot an original signal, and the corresponding denoised signal.

    idxA = 3;
plot(Espiga3(:,idxA),"r")
hold on
plot(xd(:,idxA),"b")
hold off
grid on
legend("Original","Denoised")

    Figure contains an axes object. The axes object contains 2 objects of type line. These objects represent Original, Denoised.

    Input Arguments

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    Wavelet decomposition structure of a multisignal, specified as a structure. dec is expected to be the output of mdwtdec.

    Denoising method, specified as one of the following:

    "rigrsure"

    Principle of Stein's Unbiased Risk

    "heursure"

    Heuristic variant of the "rigrsure" method

    "sqtwolog"

    Universal threshold sqrt(2*log(signalLength))

    "minimaxi"

    Minimax thresholding (see thselect)

    "penal", "penalhi", "penalme", "penallo"

    Penalization methods

    "man_thr"

    Manual method

    Optional parameter used for denoising.

    For the denoising methods "rigrsure", "heursure", "sqtwolog", and "minimaxi", param defines the multiplicative threshold rescaling, and is one of the following:

    "one"

    No rescaling

    "sln"

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

    "mln"

    Rescaling using a level dependent estimation of level noise

    For penalization methods, param specifies a sparsity parameter. The parameter, a real-valued scalar, should be such that: 1 ≤ param ≤ 10. For the penal method, no control is done.

    "penal"

    Penal

    "penalhi"

    Penal high, 2.5 ≤ param ≤ 10

    "penalme"

    Penal medium, 1.5 ≤ param ≤ 2.5

    "penallo"

    Penal low, 1 ≤ param ≤ 2

    For the manual method "man_thr", 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 (1 ≤ j ≤ NbLEV).

    • 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.

    Desired function output arguments. This option determines the output arguments of mswden and is specified as one of the following:

    • "den" — Return xd, decden, and thresh

    • "densig" — Return xd and thresh

    • "dendec" — Return decden and thresh

    • "thr" — Return thresh

    Direction indicator of the wavelet decomposition, specified as:

    • "r": Take the 1-D wavelet decomposition of each row of x

    • "c": Take the 1-D wavelet decomposition of each column of x

    Multisignal, specified as a real-valued matrix.

    Data Types: double

    Level of decomposition, specified as a positive integer. mdwtdec does not enforce a maximum level restriction. Use wmaxlev to ensure that the wavelet coefficients are free from boundary effects. If boundary effects are not a concern, a good rule is to set lev less than or equal to fix(log2(length(N))), where N is the number of samples in the 1-D data.

    Analyzing wavelet, specified as a character vector or string scalar. The wavelet must be orthogonal or biorthogonal. Orthogonal and biorthogonal wavelets are designated as type 1 and type 2 wavelets respectively in the wavelet manager, wavemngr.

    • Valid built-in orthogonal wavelet families are: Best-localized Daubechies ("bl"), Beylkin ("beyl"), Coiflets ("coif"), Daubechies ("db"), Fejér-Korovkin ("fk"), Haar ("haar"), Han linear-phase moments ("han"), Morris minimum-bandwidth ("mb"), Symlets ("sym"), and Vaidyanathan ("vaid").

    • Valid built-in biorthogonal wavelet families are: Biorthogonal Spline ("bior"), and Reverse Biorthogonal Spline ("rbio").

    For a list of wavelets in each family, see wfilters. You can also use waveinfo with the wavelet family short name. For example, waveinfo("db"). Use wavemngr("type",wn) to determine if the wavelet wn is orthogonal (returns 1) or biorthogonal (returns 2). For example, wavemngr("type","db6") returns 1.

    Output Arguments

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    Denoised multisignal, returned as a matrix. xd is a denoised version of the original multisignal x.

    Wavelet decomposition of the denoised multisignal, returned as a structure. The structure format is identical to the output of mdwtdec.

    Threshold values, returned as a matrix.

    Data Types: double

    Type of thresholding to perform, specified as one of these:

    • "s" — Soft thresholding

    • "h" — Hard thresholding

    Keep the approximation coefficients flag, specified as a numeric or logical 1 (true) or 0 (false).

    Indices of the initial signals, specified as a vector of positive integers, or "all".

    References

    [1] Birgé, L., and P. Massart. “From Model Selection to Adaptive Estimation.” Festschrift for Lucien Le Cam: Research Papers in Probability and Statistics (E. Torgersen, D. Pollard, and G. Yang, eds.). New York: Springer-Verlag, 1997, pp. 55–88.

    [2] DeVore, R. A., B. Jawerth, and B. J. Lucier. “Image Compression Through Wavelet Transform Coding.” IEEE Transactions on Information Theory. Vol. 38, Number 2, 1992, pp. 719–746.

    [3] Donoho, D. L. “Progress in Wavelet Analysis and WVD: A Ten Minute Tour.” Progress in Wavelet Analysis and Applications (Y. Meyer, and S. Roques, eds.). Gif-sur-Yvette: Editions Frontières, 1993.

    [4] Donoho, D. L., and I. M. Johnstone. “Ideal Spatial Adaptation by Wavelet Shrinkage.” Biometrika. Vol. 81, pp. 425–455, 1994.

    [5] Donoho, D. L., I. M. Johnstone, G. Kerkyacharian, and D. Picard. “Wavelet Shrinkage: Asymptopia?” Journal of the Royal Statistical Society, series B, Vol. 57, No. 2, pp. 301–369, 1995.

    [6] Donoho, D. L., and I. M. Johnstone. “Ideal denoising in an orthonormal basis chosen from a library of bases.” C. R. Acad. Sci. Paris, Ser. I, Vol. 319, pp. 1317–1322, 1994.

    [7] Donoho, D. L. “De-noising by Soft-Thresholding.” IEEE Transactions on Information Theory. Vol. 42, Number 3, pp. 613–627, 1995.

    [8] Mesa, Hector. “Adapted Wavelets for Pattern Detection.” In Progress in Pattern Recognition, Image Analysis and Applications, edited by Alberto Sanfeliu and Manuel Lazo Cortés, 3773:933–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. https://doi.org/10.1007/11578079_96.

    Version History

    Introduced in R2007a

    See Also

    Apps

    Functions