Wavelet Toolbox |

Penalized threshold for wavelet 1-D or 2-D de-noising

**Syntax **

**Description **

THR = wbmpen(C,L,SIGMA,ALPHA) returns global threshold THR for de-noising. THR is obtained by a wavelet coefficients selection rule using a penalization method provided by Birge-Massart.

[C,L] is the wavelet decomposition structure of the signal or image to be de-noised.

SIGMA is the standard deviation of the zero mean Gaussian white noise in de-noising model (see `wnoisest`

for more information).

ALPHA is a tuning parameter for the penalty term. It must be a real number greater than 1. The sparsity of the wavelet representation of the de-noised signal or image grows with ALPHA. Typically ALPHA = 2.

THR minimizes the penalized criterion given by

let t^{*} be the minimizer of^{ }

where c(k) are the wavelet coefficients sorted in decreasing order of their absolute value and n is the number of coefficients; then THR = c(t^{*}).

wbmpen(C,L,SIGMA,ALPHA,ARG) computes the global threshold and, in addition, plots three curves:

**Examples**

% Example 1: Signal de-noising. % Load noisy bumps signal. load noisbump; x = noisbump; % Perform a wavelet decomposition of the signal % at level 5 using sym6. 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 for selecting global threshold % for signal de-noising, using the tuning parameter. alpha = 2; thr = wbmpen(c,l,sigma,alpha) thr = 2.7681 % Use wdencmp for de-noising the signal using the above % threshold with soft thresholding and approximation kept. keepapp = 1; xd = wdencmp('gbl',c,l,wname,lev,thr,'s',keepapp); % Plot original and de-noised signals. figure(1) subplot(211), plot(x), title('Original signal') subplot(212), plot(xd), title('De-noised signal') % Example 2: Image de-noising. % Load original image. load noiswom; nbc = size(map,1); % Perform a wavelet decomposition of the image % at level 3 using coif2. wname = 'coif2'; lev = 3; [c,s] = wavedec2(X,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 global threshold % for image de-noising. alpha = 1.2; thr = wbmpen(c,l,sigma,alpha) thr = 36.0621 % Use wdencmp for de-noising the image using the above % thresholds with soft thresholding and approximation kept. keepapp = 1; xd = wdencmp('gbl',c,s,wname,lev,thr,'s',keepapp); % Plot original and de-noised images. figure(2) colormap(pink(nbc)); subplot(221), image(wcodemat(X,nbc)) title('Original image') subplot(222), image(wcodemat(xd,nbc)) title('De-noised image')

**See Also **

```
wden, wdencmp, wpbmpen, wpdencmp
```

waverec2 | wcodemat |