📄 threshwave2.m
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function out=ThreshWave2(Noisy,type,TI,sigma,mult,L,qmf)
% ThreshWave2 -- Denoising of 2-d image with wavelet thresholding.
% Usage
% out=ThreshWave2(Noisy,type,TI,sigma,mult,L,qmf)
% Inputs
% Noisy 2-d Noisy image, size(Noisy)= 2^J*2^J.
% type 'S' for soft thresholding, 'H' for hard thresholding.
% Optional, Default=hard thresholding.
% TI Enter 1 if you want translation-invariant denoising,
% 0 if you don't.
% Optional, Default=non-invariant.
% sigma Standard deviation of additive Gaussian White Noise.
% Enter 0 if you want sigma to be estimated by median filtering.
% Optional, Default=estimated by median filtering.
% mult Multiplier of sigma to obtain the value of the threshold.
% Optional, Default = sqrt(2*log(n)), where n is the
% number of pixels/
% L Low-Frequency cutoff for shrinkage (e.g. L=4)
% Should have L << J!
% Optional, Default = 3.
% qmf Quadrature Mirror Filter for Wavelet Transform
% Optional, Default = Symmlet 4.
% Outputs
% out Estimate, obtained by applying thresholding on
% wavelet coefficients.
%
n=length(Noisy);
if nargin < 7,
qmf = MakeONFilter('Symmlet',4);
end
if nargin < 6,
L = 3;
end
if nargin < 5,
mult = sqrt(4*log(n));
end
if nargin < 4,
[y,coef] = NormNoise(Noisy,qmf);
sigma=1/coef;
end
if nargin < 3,
TI = 0;
end
if nargin < 2,
type = 'H';
end
if sigma==0,
[y,coef] = NormNoise(Noisy,qmf);
sigma=1/coef;
end
thresh= sigma*mult;
out=zeros(1,n);
if TI==1,
if strcmp(type,'S'),
out=TIDenoiseSoft2(Noisy,L,qmf,thresh);
end
if strcmp(type,'H'),
out=TIDenoiseHard2(Noisy,L,qmf,thresh);
end
else
if strcmp(type,'S'),
out=ST2(Noisy,L,qmf,thresh);
else
out=HT2(Noisy,L,qmf,thresh);
end
end
% Written by Maureen Clerc and Jerome Kalifa, 1997
% clerc@cmapx.polytechnique.fr, kalifa@cmapx.polytechnique.fr
%
% Part of WaveLab Version 802
% Built Sunday, October 3, 1999 8:52:27 AM
% This is Copyrighted Material
% For Copying permissions see COPYING.m
% Comments? e-mail wavelab@stat.stanford.edu
%
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