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Noisy wavelet test data


X = wnoise(FUN,N)
[X,XN] = wnoise(FUN,N,SQRT_SNR)
[X,XN] = wnoise(FUN,N,SQRT_SNR,INIT)


X = wnoise(FUN,N) returns values of the test signal given by FUN, on a 2N grid of [0,1].

[X,XN] = wnoise(FUN,N,SQRT_SNR) returns a test vector X as above, rescaled such that std(X) = SQRT_SNR. The returned vector XN contains the same test vector corrupted by additive Gaussian white noise N(0,1). Then, XN has a signal-to-noise ratio of SNR = (SQRT_SNR)2.

[X,XN] = wnoise(FUN,N,SQRT_SNR,INIT) returns previous vectors X and XN, but the generator seed is set to INIT value.

The six functions below are due to Donoho and Johnstone (See “References”).

FUN = 1     or'blocks'
FUN = 2     or'bumps'
FUN = 3     or'heavy sine'
FUN = 4     or'doppler'
FUN = 5     or'quadchirp'
FUN = 6     or'mishmash'


% Generate 2^10 samples of 'Heavy sine' (item 3). 
x = wnoise(3,10); 

% Generate 2^10 samples of 'Doppler' (item 4) and of
% noisy 'Doppler' with a square root of signal-to-noise
% ratio equal to 7. 
[x,noisyx] = wnoise(4,10,7);

% To introduce your own rand seed, a fourth 
% argument is allowed: 
init = 2055415866; 
[x,noisyx] = wnoise(4,10,7,init);

% Plot all the test functions. 
ind = linspace(0,1,2^10); 
for i = 1:6 
    x = wnoise(i,10); 
    subplot(6,1,i), plot(ind,x) 

% Editing some graphical properties,
% the following figure is generated.


Donoho, D.L.; I.M. Johnstone (1994), “Ideal spatial adaptation by wavelet shrinkage,” Biometrika, vol. 81, pp. 425–455.

Donoho, D.L.; I.M. Johnstone (1995), “Adapting to unknown smoothness via wavelet shrinkage via wavelet shrinkage,” JASA, vol. 90, 432, pp. 1200–1224.

See Also

Introduced before R2006a

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