wbmpen

Penalized threshold for wavelet 1-D or 2-D denoising

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    Syntax

    thr = wbmpen(c,l,sigma,alpha)
    thr = wbmpen(c,l,sigma,alpha,ARG)

    Description

    thr = wbmpen(c,l,sigma,alpha) returns the global threshold thr for denoising. c,l is the wavelet decomposition structure of the signal or image to be denoised. sigma is the standard deviation of the zero mean Gaussian white noise in the denoising model (see wnoisest for more information). alpha is a tuning parameter for the penalty term.

    example

    thr = wbmpen(c,l,sigma,alpha,ARG) computes the global threshold and plots three curves:

    • 2×sigma^2×t×(alpha + log(n/t))

    • sum(c(k)^2,k≤t)

    • crit(t)

    where n is the number of coefficients and 

    crit(t) = -sum(c(k)^2,k≤t) + 2×sigma^2×t×(alpha + log(n/t)).
    For more information, see Penalized Criterion.

    Examples

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    Open Live Script

    Load the noisy bumps signal.

    load noisbump
x = noisbump;

    Perform a wavelet decomposition of the signal at level 5 using the sym6 wavelet.

    wname = "sym6";
lev = 5;
[c,l] = wavedec(x,lev,wname);

    Estimate the noise standard deviation from the detail coefficients at level 1, using wnoisest.

    sigma = wnoisest(c,l,1);

    Use wbmpen to obtain a global threshold for signal thresholding, using the tuning parameter.

    alpha = 2;
thr = wbmpen(c,l,sigma,alpha)
    thr = 
2.7681

    Use wdencmp for denoising the signal using the threshold with soft thresholding and approximation kept.

    keepapp = 1;
xd = wdencmp("gbl",c,l,wname,lev,thr,"s",keepapp);

    Plot the original and denoised signals.

    tiledlayout(2,1)
nexttile
plot(x)
axis tight
title("Original Signal")
nexttile
plot(xd)
axis tight
title("Denoised Signal")

    Figure contains 2 axes objects. Axes object 1 with title Original Signal contains an object of type line. Axes object 2 with title Denoised Signal contains an object of type line.

    Open Live Script

    Load an image.

    load flower
img = flower.Noisy;

    Perform a wavelet decomposition of the image at level 3 using the coif2 wavelet.

    wname = "coif2";
lev = 3;
[c,s] = wavedec2(img,lev,wname);

    Estimate the noise standard deviation from the detail coefficients at level 1.

    det1 = detcoef2("compact",c,s,1);
sigma = median(abs(det1))/0.6745;

    Use wbmpen for selecting a global threshold for image denoising.

    alpha = 1.2;
thr = wbmpen(c,s,sigma,alpha)
    thr = 
0.2288

    Use wdencmp for denoising the image using the threshold with soft thresholding and approximation kept.

    keepapp = 1;
xd = wdencmp("gbl",c,s,wname,lev,thr,"s",keepapp);

    Plot the original and denoised images.

    tiledlayout(1,2)
colormap(gray)
nexttile
imagesc(img)
title("Original Image")
nexttile
imagesc(xd)
title("Denoised Image")

    Figure contains 2 axes objects. Axes object 1 with title Original Image contains an object of type image. Axes object 2 with title Denoised Image contains an object of type image.

    Input Arguments

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    Wavelet decomposition structure of a signal or image, specified by the vectors c and l. The vector c contains the wavelet coefficients. The bookkeeping vector l contains the number of coefficients by level. For more information, see wavedec and wavedec2.

    Data Types: double

    Standard deviation of the zero mean Gaussian white noise in the denoising model, specified as a scalar. For more information, see wnoisest and Penalized Criterion.

    Data Types: double

    Tuning parameter for the penalty term, specified as a scalar greater than 1. The sparsity of the wavelet representation of the denoised signal or image grows with alpha. Typically, alpha = 2. For more information, see Penalized Criterion.

    Data Types: double

    Output Arguments

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    Global threshold for denoising, returned as a scalar. thr is obtained by a wavelet coefficients selection rule using a penalization method provided by Birgé-Massart. For more information, see Penalized Criterion.

    More About

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    References

    [1] Birgé, Lucien, and Pascal Massart. “From Model Selection to Adaptive Estimation.” In Festschrift for Lucien Le Cam, edited by David Pollard, Erik Torgersen, and Grace L. Yang, 55–87. New York, NY: Springer New York, 1997. https://doi.org/10.1007/978-1-4612-1880-7_4.

    Version History

    Introduced before R2006a

    See Also

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