小波软硬及折中阈值法图像去噪

%zheZhongYuZhi.m %%%比较软硬阈值处理方法及其折中 clear clc X=imread('Miss.bmp'); %读入要处理的图像 %X=im2double(X); A=imnoise(X,'gaussian',0,0.025); %给图像加噪声 [c,s]=wavedec2(A,3,'sym6'); %进行3层小波分解 num(1,1)=s(1,1)*s(1,2)+1; %获取各层各高频分量在c向量中的坐标 num(1,2)=num(1,1)+s(2,1)*s(2,2); num(1,3)=num(1,2)+s(2,1)*s(2,2); step(1)=s(2,1)*s(2,2); num(2,1)=num(1,3)+s(2,1)*s(2,2); num(2,2)=num(2,1)+s(3,1)*s(3,2); num(2,3)=num(2,2)+s(3,1)*s(3,2); step(2)=s(3,1)*s(3,2); num(3,1)=num(2,3)+s(3,1)*s(3,2); num(3,2)=num(3,1)+s(4,1)*s(4,2); num(3,3)=num(3,2)+s(4,1)*s(4,2); step(3)=s(4,1)*s(4,2); C=c; for j=1:101 b(j)=0.01*(j-1); %折中系数取0到1中的两位小数进行比较 for i=1:3 [H,V,D]=detcoef2('a',c,s,i); %提取第i层各高频系数 sigmah=median(median(abs(H)))/0.6745; %计算阈值 sigmav=median(median(abs(V)))/0.6745; sigmad=median(median(abs(D)))/0.6745; [M,N]=size(H); thh=sigmah*sqrt(2*log10(M*N)); thv=sigmav*sqrt(2*log10(M*N)); thd=sigmad*sqrt(2*log10(M*N)); ch=c(1,num(4-i,1):num(4-i,2)+step(4-i)-1);%对各高频系数进行阈值处理 C(1,num(4-i,1):num(4-i,2)+step(4-i)-1)=yuzhicl(ch,thh,b(j));%阈值处理函数 cv=c(1,num(4-i,2):num(4-i,2)+step(4-i)-1); C(1,num(4-i,2):num(4-i,2)+step(4-i)-1)=yuzhicl(cv,thh,b(j)); cd=c(1,num(4-i,3):num(4-i,2)+step(4-i)-1); C(1,num(4-i,3):num(4-i,2)+step(4-i)-1)=yuzhicl(cd,thh,b(j)); end X0(:,:,j)=waverec2(C,s,'sym6');%重构图像 sndz(j)=10*log10(sum(sum(double(X).^2))/sum(sum((double(X)-double(X0(:,:,j))).^2)));%计算信噪比 end disp('原图像信噪比为：'),sny=10*log10(sum(sum(double(X).^2))/sum(sum((double(X)-double(A)).^2))) disp('硬阈值去噪后图像信噪比为：'),sndz(1) disp('软阈值去噪后图像信噪比为：'),sndz(101) [ma,n]=max(sndz); %获取最佳图像及其坐标 disp('折中去噪后图像信噪比为：'),ma disp('信噪比提高：'),shen=ma-sny disp('最佳折中系数为：'),b(n) figure(1) imshow(X) title('原图像')%显示原图像 figure(2) imshow(A,[]) title('添加噪声后的图像')%显示加噪图像, figure(3) imshow(X0(:,:,1),[]) title('硬阈值去噪后图像')%显示硬阈值去噪后的图像 figure(4) imshow(X0(:,:,101),[]) title('软阈值去噪后图像')%显示软阈值去噪后的图像 figure(5) imshow(X0(:,:,n),[]) title('折中去噪后图像')%显示折中去噪后的图像 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%