rationallogist3.m

这么代码是用Matlab编写的

clc;
clear all;

Ximage=double(imread('e:\tu\house.jpg'));
key0=0.1;
key1=4;
[M,N]=size(Ximage);
figure(1)
Ximage=uint8(Ximage);
imshow(Ximage,[]);
Ximage=double(Ximage);

for(i=1:M)
    for(j=1:N)
       key0=key1*key0*(1-key0);
       a(i,j)=key0;
    end
end

key3=0.2;
key2=4;
for(i=1:M)
    for(j=1:N)
        key3=key2*key3*(1-key3);
        b(i,j)=key3;
    end
end

key4=0.3;
key2=4;
for(i=1:M)
    for(j=1:N)
        key4=key2*key4*(1-key4);
        c(i,j)=key4;
    end
end

t=0.4;
w0=0.2;
w1=0.5;
w2=0.3;
w=(1-t)^2*w0+2*t*(1-t)*w1+t^2*w2;
for(i=1:M)
   for(j=1:N)  
        P(i,j)=(1-t)^2*a(i,j)*w0+2*t*(1-t)*b(i,j)*w1+t^2*c(i,j)*w2;
%        P(i,j)=(1-t)^2*a(i,j)*w0+2*t*(1-t)*b(i,j)*w1+t^2*c(i,j)*w2;
       d(i,j)=P(i,j)/w;
    end
end
x=d;
e=round(x*256);
% plot(d);
tt=0.1;
Yimage=mod(tt*Ximage+(1-tt)*e,256);
figure(2)
Yimage=uint8(Yimage);
imshow(Yimage,[]);
 Yimage=double(Yimage);
 
%  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%分        析
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%置   乱    度   分    析
ssy=0; 
% for(i=1:M)%置乱后图像的均值
%      for(j=1:N)
%          ssy=Yimage(i,j)+ssy;         
%      end
%  end
ssy=sum(sum(Yimage));
 uy=ssy/(M*N);%置乱后图像的均值
 vy=sum(sum((Yimage-uy)^2));
 ssx=sum(sum(Ximage));
 ux=ssx/(M*N);%原图像的均值
 vx=sum(sum((Ximage-ux)^2));
  Variancey=vy/uy;%置乱后图像的方差
 Variancex=vx/ux;%原图像的方差
 DDD=Variancey/Variancex;%置乱度

%                    相关性分析

%%%%%%%%%%%%%%%%%%%%%%%%%%%%回              复
% Yimage=mod(tt*Ximage+(1-tt)*e,256);
Zimage=(Yimage-(1-tt)*e)/tt;
figure(3)
imshow(Zimage,[]);