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Highlights from
Wavelets Based Denoising

Wavelets Based Denoising

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25 Nov 2004 (Updated )

This program shows wavelets based denoising of audio file and arbitrary signals.

denoise(x,h,type,option)
function [xd,xn,option] = denoise(x,h,type,option)
%    [xd,xn,option] = denoise(x,h,type,option); 
%
%    DENOISE is a generic program for wavelet based denoising.
%    The program will denoise the signal x using the 2-band wavelet
%    system described by the filter h using either the traditional 
%    discrete wavelet transform (DWT) or the linear shift invariant 
%    discrete wavelet transform (also known as the undecimated DWT
%    (UDWT)). 
%
%    Input:  
%       x         : 1D or 2D signal to be denoised
%       h         : Scaling filter to be applied
%       type      : Type of transform (Default: type = 0)
%                   0 --> Discrete wavelet transform (DWT)
%                   1 --> Undecimated DWT (UDWT)
%       option    : Default settings is marked with '*':
%                   *type = 0 --> option = [0 3.0 0 0 0 0]
%                   type = 1 --> option = [0 3.6 0 1 0 0]
%       option(1) : Whether to threshold low-pass part
%                   0 --> Don't threshold low pass component 
%                   1 --> Threshold low pass component
%       option(2) : Threshold multiplier, c. The threshold is
%                   computed as: 
%                     thld = c*MAD(noise_estimate)). 
%                   The default values are:
%                     c = 3.0 for the DWT based denoising
%                     c = 3.6 for the UDWT based denoising
%       option(3) : Type of variance estimator
%                   0 --> MAD (mean absolute deviation)
%                   1 --> STD (classical numerical std estimate)
%       option(4) : Type of thresholding
%                   0 --> Soft thresholding
%                   1 --> Hard thresholding
%       option(5) : Number of levels, L, in wavelet decomposition. By
%                   setting this to the default value '0' a maximal
%                   decomposition is used.
%       option(6) : Actual threshold to use (setting this to
%                   anything but 0 will mean that option(3)
%                   is ignored)
%
%    Output: 
%       xd     : Estimate of noise free signal 
%       xn     : The estimated noise signal (x-xd)
%       option : A vector of actual parameters used by the
%                program. The vector is configured the same way as
%                the input option vector with one added element
%                option(7) = type.
%
%  HERE'S AN EASY WAY TO RUN THE EXAMPLES:
%  Cut-and-paste the example you want to run to a new file 
%  called ex.m, for example. Delete out the % at the beginning 
%  of each line in ex.m (Can use search-and-replace in your editor
%  to replace it with a space). Type 'ex' in matlab and hit return.
%
%    Example 1: 
%       h = daubcqf(6); [s,N] = makesig('Doppler'); n = randn(1,N);
%       x = s + n/10;     % (approximately 10dB SNR)
%       figure;plot(x);hold on;plot(s,'r');
%
%       %Denoise x with the default method based on the DWT
%       [xd,xn,opt1] = denoise(x,h);
%       figure;plot(xd);hold on;plot(s,'r');
%
%       %Denoise x using the undecimated (LSI) wavelet transform
%       [yd,yn,opt2] = denoise(x,h,1);
%       figure;plot(yd);hold on;plot(s,'r');
%
% Example 2: (on an image)  
%      h = daubcqf(6);  load lena; 
%      noisyLena = lena + 25 * randn(size(lena));
%      figure; colormap(gray); imagesc(lena); title('Original Image');
%       figure; colormap(gray); imagesc(noisyLena); title('Noisy Image'); 
%       Denoise lena with the default method based on the DWT
%      [denoisedLena,xn,opt1] = denoise(noisyLena,h);
%      figure; colormap(gray); imagesc(denoisedLena); title('denoised Image');
%       
%
%    See also: mdwt, midwt, mrdwt, mirdwt, SoftTh, HardTh, setopt
%

%File Name: denoise.m
%Last Modification Date: 04/15/97	10:44:28
%Current Version: denoise.m	2.4
%File Creation Date: Mon Feb 20 08:33:15 1995
%Author: Jan Erik Odegard  <odegard@ece.rice.edu>
%
%Copyright (c) 2000 RICE UNIVERSITY. All rights reserved.
%Created by Jan Erik Odegard, Department of ECE, Rice University. 
%
%This software is distributed and licensed to you on a non-exclusive 
%basis, free-of-charge. Redistribution and use in source and binary forms, 
%with or without modification, are permitted provided that the following 
%conditions are met:
%
%1. Redistribution of source code must retain the above copyright notice, 
%   this list of conditions and the following disclaimer.
%2. Redistribution in binary form must reproduce the above copyright notice, 
%   this list of conditions and the following disclaimer in the 
%   documentation and/or other materials provided with the distribution.
%3. All advertising materials mentioning features or use of this software 
%   must display the following acknowledgment: This product includes 
%   software developed by Rice University, Houston, Texas and its contributors.
%4. Neither the name of the University nor the names of its contributors 
%   may be used to endorse or promote products derived from this software 
%   without specific prior written permission.
%
%THIS SOFTWARE IS PROVIDED BY WILLIAM MARSH RICE UNIVERSITY, HOUSTON, TEXAS, 
%AND CONTRIBUTORS AS IS AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, 
%BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS 
%FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL RICE UNIVERSITY 
%OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 
%EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 
%PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; 
%OR BUSINESS INTERRUPTIONS) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 
%WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR 
%OTHERWISE), PRODUCT LIABILITY, OR OTHERWISE ARISING IN ANY WAY OUT OF THE 
%USE OF THIS SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
%
%For information on commercial licenses, contact Rice University's Office of 
%Technology Transfer at techtran@rice.edu or (713) 348-6173
%
%Change History: Fixed output of function and an error in the computation
%                of the threshold for redundant denoising.
%                <Jan Erik Odegard> <Mon Jul 31, 1995>
%
%                This code is composed of several of our old codes for
%                wavelet based denoising. In an effort to make the mess
%                more manageable we decided to create on code that would 
%                handle all the various wavelet based denoising methods.
%                However, only time will show (as we discover new and 
%                improved forms of denoising) if we can succeed in our goals.
%                <Jan Erik Odegard> <Thu May 11, 1995>
%

if(nargin < 2)
  error('You need to provide at least 2 inputs: x and h');
end;
if(nargin < 3),
  type = 0;
  option = [];
elseif(nargin < 4)
  option = [];
end;
if(isempty(type)),
  type = 0;
end;
if(type == 0),
  default_opt = [0 3.0 0 0 0 0];
elseif(type == 1),
  default_opt = [0 3.6 0 1 0 0];
else,
  error(['Unknown denoising method',10,...
	  'If it is any good we need to have a serious talk :-)']);
end;
option = setopt(option,default_opt);
[mx,nx] = size(x);
dim = min(mx,nx);
if(dim == 1),
  n = max(mx,nx);
else,
  n = dim;
end;
if(option(5) == 0),
  L = floor(log2(n));
else
  L = option(5);
end;
if(type == 0), 			% Denoising by DWT
  xd = mdwt(x,h,L);
  if (option(6) == 0),
    tmp = xd(floor(mx/2)+1:mx,floor(nx/2)+1:nx);
    if(option(3) == 0),
      thld = option(2)*median(abs(tmp(:)))/.67;
    elseif(option(3) == 1),
      thld = option(2)*std(tmp(:));
    else
      error('Unknown threshold estimator, Use either MAD or STD');
    end;
  else,
    thld = option(6);
  end;
  if(dim == 1)
    ix = 1:n/(2^L);
    ykeep = xd(ix);
  else
    ix = 1:mx/(2^L);
    jx = 1:nx/(2^L);
    ykeep = xd(ix,jx);
  end;
  if(option(4) == 0),
    xd = SoftTh(xd,thld);
  elseif(option(4) == 1),
    xd = HardTh(xd,thld);
  else,
    error('Unknown threshold rule. Use either Soft (0) or Hard (1)');
  end;
  if (option(1) == 0),
    if(dim == 1),
      xd(ix) = ykeep;
    else,
      xd(ix,jx) = ykeep;
    end;
  end;
  xd = midwt(xd,h,L);
elseif(type == 1), 			% Denoising by UDWT
  [xl,xh] = mrdwt(x,h,L);
  if(dim == 1),
    c_offset = 1;
  else,
    c_offset = 2*nx + 1;
  end;
  if (option(6) == 0),
    tmp = xh(:,c_offset:c_offset+nx-1);
    if(option(3) == 0),
      thld = option(2)*median(abs(tmp(:)))/.67;
    elseif(option(3) == 1),
      thld = option(2)*std(tmp(:));
    else
      error('Unknown threshold estimator, Use either MAD or STD');
    end;
  else,
    thld = option(6);
  end;
  if(option(4) == 0),
    xh = SoftTh(xh,thld);
    if(option(1) == 1),
      xl = SoftTh(xl,thld);
    end;
  elseif(option(4) == 1),
    xh = HardTh(xh,thld);
    if(option(1) == 1),
      xl = HardTh(xl,thld);
    end;
  else,
    error('Unknown threshold rule. Use either Soft (0) or Hard (1)');
  end;
  xd = mirdwt(xl,xh,h,L);
else, 					% Denoising by unknown method
  error(['Unknown denoising method',10,...
         'If it is any good we need to have a serious talk :-)']);
end;
option(6) = thld;
option(7) = type;
xn = x - xd; 

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