Penalized threshold for wavelet packet de-noising
THR = wpbmpen(T,SIGMA,ALPHA) returns a global threshold THR for de-noising. THR is obtained by a wavelet packet coefficients selection rule using a penalization method provided by Birge-Massart.
T is a wavelet packet tree corresponding to the wavelet packet decomposition of the signal or image to be de-noised.
SIGMA is the standard deviation of the zero mean Gaussian white noise in the 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 packet 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 packet coefficients sorted in decreasing order of their absolute value and n is the number of coefficients, then THR = c(t*).
wpbmpen(T,SIGMA,ALPHA,ARG) computes the global threshold and, in addition, plots three curves:
% Example 1: Signal de-noising. % Load noisy chirp signal. load noischir; x = noischir; % Perform a wavelet packet decomposition of the signal % at level 5 using sym6. wname = 'sym6'; lev = 5; tree = wpdec(x,lev,wname); % Estimate the noise standard deviation from the % detail coefficients at level 1, % corresponding to the node index 2. det1 = wpcoef(tree,2); sigma = median(abs(det1))/0.6745; % Use wpbmpen for selecting global threshold % for signal de-noising, using the recommended parameter. alpha = 2; thr = wpbmpen(tree,sigma,alpha) thr = 4.5740 % Use wpdencmp for de-noising the signal using the above % threshold with soft thresholding and keeping the approximation. keepapp = 1; xd = wpdencmp(tree,'s','nobest',thr,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 packet decomposition of the image % at level 3 using coif2. wname = 'coif2'; lev = 3; tree = wpdec2(X,lev,wname); % Estimate the noise standard deviation from the % detail coefficients at level 1. det1 = [wpcoef(tree,2) wpcoef(tree,3) wpcoef(tree,4)]; sigma = median(abs(det1(:)))/0.6745; % Use wpbmpen for selecting global threshold % for image de-noising. alpha = 1.1; thr = wpbmpen(tree,sigma,alpha) thr = 38.5125 % Use wpdencmp for de-noising the image using the above % thresholds with soft thresholding and keeping the approximation. keepapp = 1; xd = wpdencmp(tree,'s','nobest',thr,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')
wbmpen, wden, wdencmp, wpdencmp