This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English verison of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

Discrete Wavelet Analysis

Wavelet Toolbox™ software enables you to analyze signals, images, and 3-D data using orthogonal and biorthogonal critically-sampled discrete wavelet analysis. Critically-sampled discrete wavelet analysis is also known as decimated discrete wavelet analysis. Decimated discrete wavelet analysis is most appropriate for data compression, denoising, and the sparse representation of certain classes of signals and images.

In decimated discrete wavelet analysis, the scales and translations are dyadic.

You can perform 1-D, 2-D, and 3-D decimated discrete wavelet analysis using the interactive tool by entering waveletAnalyzer at the command line and clicking Wavelet 1-D, Wavelet 2-D, or Wavelet 3-D.

1-D Wavelet Denoising

This example shows how to denoise a signal using discrete wavelet analysis.

Create a reference signal.

len = 2^11;
h = [4  -5  3  -4  5  -4.2   2.1   4.3  -3.1   5.1  -4.2];
t = [0.1  0.13  0.15  0.23  0.25  0.40  0.44  0.65  0.76  0.78  0.81];
h  = abs(h);
        w  = 0.01*[0.5 0.5 0.6 1 1 3 1 1 0.5 0.8 0.5];
        tt = linspace(0,1,len);  xref = zeros(1,len);
        for j=1:11
            xref = xref + ( h(j) ./ (1+ ((tt-t(j))/w(j)).^4));

Add zero-mean white Gaussian noise with a variance of 0.25.

rng default;
x = xref + 0.5*randn(size(xref));
plot(x); set(gca,'xlim',[1 2048]);

Denoise the signal down to level 3 using the Daubechies least asymmetric wavelet with 4 vanishing moments. Use the universal threshold selection rule of Donoho and Johnstone with soft thresholding based on the DWT coefficients at level 1. Use the periodization signal extension mode — dwtmode('per'). Plot the result along with the reference signal for comparision.

**  DWT Extension Mode: Periodization  **
[xd,cxd,lxd] = wden(x,'sqtwolog','s','sln',4,'sym4');
set(gca,'xlim',[1 2048]); hold on;

2-D Decimated Discrete Wavelet Analysis

This example shows how to obtain the 2-D DWT of an input image.

Load and display the image. The image consists of vertical, horizontal, and diagonal patterns.

load tartan;
imagesc(X); colormap(gray);

Obtain the 2-D DWT at level 1 using the biorthogonal B-spline wavelet and scaling filters with 2 vanishing moments in the analysis filters and 4 vanishing moments in the synthesis filters. Extract the horizontal, vertical, and diagonal wavelet coefficients and the approximation coefficients. Display the results.

[C,S] = wavedec2(X,1,'bior2.4');
[H,V,D] = detcoef2('all',C,S,1);
A = appcoef2(C,S,'bior2.4');
imagesc(A); title('Approximation Level 1');
imagesc(H); title('Horizontal Details');
imagesc(V); title('Vertical Details');
imagesc(D); title('Diagonal Details');

You see that the wavelet details are sensitive to particular orientations in the input image. The approximation coefficients are a lowpass approximation to the original image.

Nondecimated Discrete Wavelet Analysis

This example shows how to obtain the nondecimated (stationary) wavelet transform of a noisy frequency-modulated signal.

Load the noisy Doppler signal and obtain the stationary wavelet transform down to level 4.

load noisdopp;
swc = swt(noisdopp,4,'sym8');

Plot the original signal and the level 1 and 3 wavelet coefficients. Plot the level 4 approximation.

plot(swc(1,:)); ylabel('D1');
plot(swc(3,:)); ylabel('D3');
plot(swc(5,:)); ylabel('A4');

The wavelet and approximation coefficients at each level are equal in length to the input signal. The additive noise is almost entirely localized in the level one detail coefficients. The level 3 details coefficients captures the high-frequency oscillations at the beginning of the Doppler signal. The level 4 approximation coefficients are a lowpass approximation to the Doppler signal.

Obtain the 2-D nondecimated wavelet transform of an image. Use the Daubechies least asymmetric wavelet, sym4, and obtain the multiresolution analysis down to level 3. Load the image. Use wcodemat to scale the matrix for display.

load tartan;
nbcol = size(map,1);
cod_X = wcodemat(X,nbcol);

Obtain the nondecimated multiresolution analysis down to level 3.

[ca,chd,cvd,cdd] = swt2(X,3,'sym4');

Display the original image and the approximation and detail coefficients at each level.

title('Original image');

for k = 1:3
    cod_ca  = wcodemat(ca(:,:,k),nbcol);
    cod_chd = wcodemat(chd(:,:,k),nbcol);
    cod_cvd = wcodemat(cvd(:,:,k),nbcol);
    cod_cdd = wcodemat(cdd(:,:,k),nbcol);
    decl = [cod_ca,cod_chd;cod_cvd,cod_cdd];


    title(['SWT dec.: approx. ', ...
   'and det. coefs (lev. ',num2str(k),')']);

Was this topic helpful?