cdenoisegaussian.m

CONTOURLET去噪。添加了高斯噪声很好的实现了图像的多尺度分解下的去噪与重建。实现了多尺度分解的子带去噪。

% function contour denoise & enhance
%%% Cdenoise gaussian 仅仅是C分解、重构滤波，采用阈值而已。

%%%%%%%%%%%%%%%%%%%%contourlet去噪 add noise and denoise
clc
clear all

a=imread('barbara.png');
% aa=rgb2gray(a);
figure,imshow(a);title('原始图像');
im=double(a);
% 
% th=30; 
% rho=10;

% Generate noisy image. 
% noiseim=imnoise(a,'salt & pepper',0.05);
sigema=0.098;
noiseim=imnoise(a,'gaussian',0,sigema)
im2uint8(0.018)  %%%5
im2uint8(0.04)  %%%10
im2uint8(0.058)  %%%15
im2uint8(0.08) %%20
im2uint8(0.098)  %%%25
figure,imshow(noiseim);title('噪声图像');

%%%%contourlet transform
nlevels=[1,2,3];
pfilter = '9-7' ;
dfilter = 'pkva' ;
coeffs = pdfbdec( double(noiseim), pfilter, dfilter, nlevels );
% [c,s]=pdfb2vec(coeffs);
%%%%%%%%%%现在工件表面做三层LP分解，方向数分别为2的一次方，2的二次方，2的三次方
y1=coeffs{1,1};
y2=coeffs{1,2};
y3=coeffs{1,3};
y4=coeffs{1,4};
%%%%%%%%%%%%%%%
%%%%第二层子带di er ceng de liang ge zi dai
y21=y2{1,1};
y22=y2{1,2};
%%%%%第三层子带di san ceng de sige zidai
y31=y3{1,1};
y32=y3{1,2};
y33=y3{1,3};
y34=y3{1,4};
%%%%%第四层子带di si ceng de ba ge zidai
y41=y4{1,1};
y42=y4{1,2};
y43=y4{1,3};
y44=y4{1,4};
y45=y4{1,5};
y46=y4{1,6};
y47=y4{1,7};
y48=y4{1,8};

%%%%%%%%%% calculate the y41-y48 PCA modify the value:
%%%%%%%%%55
%%%%%%%%%%%55 y41
th=sigema.^0.5*(2*64*128).^0.5;

for i=1:64;j=1:128;
    k41=find(y41<th);
    y41(k41)=0;
    Y41=y41;
    
    k42=find(y42<th);
    y42(k42)=0;
    Y42=y42;
    
    k43=find(y43)<th;
    y43(k43)=0; 
    Y43=y43;
    
    k44=find(y44)<th;
    y44(k44)=0; 
    Y44=y44; 
end

for i=1:128;j=1:64;
   
    k45=find(y45<th);
    y45(k45)=0;
    Y45=y45;
    
    k46=find(y46)<th;
    y46(k46)=0; 
    Y46=y46;
    
    k47=find(y47)<th;
    y47(k47)=0; 
    Y47=y47;
    
    k48=find(y48)<th;
    y48(k48)=0; 
    Y48=y48;
end

Y=cell(1,4);
Y{1,1}=y1;
Y{1,2}=y2;
Y{1,3}=y3;
% 
y4{1,1}=Y41;
y4{1,2}=Y42;
y4{1,3}=Y43;
y4{1,4}=Y44;
y4{1,5}=Y45;
y4{1,6}=Y46;
y4{1,7}=Y47;
y4{1,8}=Y48;

Y{1,4}=y4;

% Reconstruction% Reconstruction% Reconstruction
cim = pdfbrec(Y, pfilter, dfilter);%%%%%%% use the modified cofficients to reconstruction

newim=adjgamma(cim,1);
figure,imshow(newim);
title(' CPCA去噪图像');

% pp=double(noiseim)-newim;
pp=im-newim;
for i=1:256;j=1:256;
    P(i,j)=pp(i,j).^2;
    MSE=sum(P(:))/(256*256);
end
PSNR=20*log( (255.^2) /   MSE );
PSNR