www.pudn.com > wavelet_transf.rar > wavelet.m

               %WAVELET BASED COMPRESSION   
               %**************************** 
 
%removes all variables, globals, functions and 
%MEX links(MATLAB loads and runs a different entry point symbol for C or Fortran MEX-files) 
clear all; 
% CLOSE ALL  closes all the open figure windows. 
close all; 
%read the image 
input_image1=imread('rice.tif'); 
%display input image 
%add noise 
input_image=imnoise(input_image1,'speckle',.01); 
figure; 
imshow(input_image); 
%give the number of decomposition level which must be integer and should not exceed 3 
n=input('enter the decomposition level'); 
 
%***************************************************************************** 
[Lo_D,Hi_D,Lo_R,Hi_R] = wfilters('haar'); 
% computes four filters associated with the orthogonal or biorthogonal 
%  wavelet named in the string 'wname'.  
%    The four output filters are: 
 %       LO_D, the decomposition low-pass filter 
 %       HI_D, the decomposition high-pass filter 
  %      LO_R, the reconstruction low-pass filter 
   %     HI_R, the reconstruction high-pass filter 
   % Available wavelet names 'wname' are: 
   % Daubechies: 'db1' or 'haar', 'db2', ... ,'db45' 
    %Coiflets  : 'coif1', ... ,  'coif5' 
    %Symlets   : 'sym2' , ... ,  'sym8', ... ,'sym45' 
    %Discrete Meyer wavelet: 'dmey' 
%**********************************************************************     
 
%wavedec2    - Multi-level 2-D wavelet decomposition. 
[c,s]=wavedec2(input_image,n,Lo_D,Hi_D); 
 
% gives the wavelet decomposition of the matrix input_image at level n, using the 
% wavelet named in string 'wname' or low pass and high pass 
% Outputs are the decomposition vector C and the 
%corresponding bookkeeping matrix S. 
disp(' the decomposition vector Output is'); 
disp(c); 
%************************************************************************************* 
  
%Thresholds for wavelet 2-D using Birge-Massart strategy. 
[thr,nkeep] = wdcbm2(c,s,1.5,3*prod(s(1,:))); 
 
 % give level-dependent thresholds 'thr'and numbers of coefficients to be kept 'nkeep' 
 % for compression. 'thr' is obtained using a wavelet coefficients  
 %selection rule based on Birge-Massart strategy. 
  %disp('level-dependent thresholds'); 
 %disp(thr); 
 %disp(' numbers of coefficients to be'); 
 %disp(nkeep); 
%*************************************************************************** 
  %compression using wavelet packets. 
 [compressed_image,TREED,comp_ratio,PERFL2] =WPDENCMP(thr,'s',n,'haar','threshold',5,1); 
 disp('compression ratio in percentage'); 
 disp(comp_ratio); 
 % returns a compressed version compressed_image of input 
 % signal 'thr' (2-D) obtained by wavelet packet  coefficients thresholding. 
  %  The additional output argument TREED is the 
  %  wavelet packet best tree decomposition of compressed_image. 
  %  PERFL2 and PERF0 are L^2 recovery and compression scores in percentages. 
   
  %Multi-level 2-D wavelet reconstruction. 
 re_ima1 = waverec2(c,s,'haar');  
 re_ima=uint8(re_ima1); 
  subplot(1,3,1); 
 imshow(input_image); 
 title('i/p image'); 
 subplot(1,3,2); 
 imshow(compressed_image); 
 title('compressed image'); 
 subplot(1,3,3); 
 imshow(re_ima); 
 title('reconstructed image');