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代码.txt
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1、
添加高斯噪声
I=imread(' E:\课程\数字图像处理\作业\第六次作业\lena.bmp');
figure(1);
imshow(I);
title('源图像lena.bmp');
imwrite(I,'lena原始图像.bmp');
I2=imnoise(I,'gaussian',0.5,0.01);
figure(2);
imshow(I2);
title('加入gaussian噪声后的lena.bmp');
imwrite(I2,'lena加入gaussian噪声后(u=0.5，s^2=0.01).bmp');
算术均值滤波器恢复图像
I=imread(' E:\课程\数字图像处理\作业\第六次作业\lena加入gaussian噪声后(u=0，s^2=0.01).bmp');
figure(1);
imshow(I);
title('lena加入gaussian噪声后(u=0，s^2=0.01).bmp');
imwrite(I,'lena加入gaussian噪声后(u=0，s^2=0.01).bmp');
n=5;
h=1/n^2.*ones(n,n);
I2=conv2(I,h,'same');
I2=uint8(I2);
figure(2);
imshow(I2);
title('算术均值滤波的结果（5x5）');
imwrite(I2,'算术均值滤波的结果（5x5）.bmp');
中值滤波器恢复图像
I=imread('E:\课程\数字图像处理\作业\第六次作业\lena加入gaussian噪声后(u=0，s^2=0.01).bmp');
figure(1);
imshow(I);
title('lena加入gaussian噪声后(u=0，s^2=0.01).bmp');
imwrite(I,'lena加入gaussian噪声后(u=0，s^2=0.01).bmp');
figure(2);
n=5;
a=ones(n,n);
p=size(I);
x1=double(I);
x2=x1;
for i=1:p(1)-n+1
for j=1:p(2)-n+1
c=x1(i:i+(n-1),j:j+(n-1));
e=c(1,:);
for u=2:n
e=[e,c(u,:)];
end
mm=median(e);
x2(i+(n-1)/2,j+(n-1)/2)=mm;
end
end
I2=uint8(x2);
imshow(I2);
title('中值滤波的结果（5x5）');
imwrite(I2,'中值滤波的结果（5x5）.bmp');
谐波均值滤波恢复图像
f = imread('E:\课程\数字图像处理\作业\第六次作业\lena加入椒盐噪声后.bmp');
f = mat2gray(f,[0 255]);
[M,N] = size(f);
g_Ex = zeros(M+2,N+2);
for x = 1:M
g_Ex(x+1,:) = [f(x,1) f(x,:) f(x,N)];
end
g_Ex(1,:) = g_Ex(2,:);
g_Ex(M+2,:) = g_Ex(M+1,:);
g_hmf = zeros(M,N);
for x = 2:M+1
for y = 2:N+1
g_hmf(x-1,y-1) = 1/g_Ex(x ,y);
g_hmf(x-1,y-1) = g_hmf(x-1,y-1) + 1/g_Ex(x-1,y);
g_hmf(x-1,y-1) = g_hmf(x-1,y-1) + 1/g_Ex(x ,y-1);
g_hmf(x-1,y-1) = g_hmf(x-1,y-1) + 1/g_Ex(x+1,y);
g_hmf(x-1,y-1) = g_hmf(x-1,y-1) + 1/g_Ex( x,y+1);
g_hmf(x-1,y-1) = g_hmf(x-1,y-1) + 1/g_Ex(x-1,y+1);
g_hmf(x-1,y-1) = g_hmf(x-1,y-1) + 1/g_Ex(x+1,y-1);
g_hmf(x-1,y-1) = g_hmf(x-1,y-1) + 1/g_Ex(x+1,y+1);
g_hmf(x-1,y-1) = g_hmf(x-1,y-1) + 1/g_Ex(x-1,y-1);
g_hmf(x-1,y-1) = 9/g_hmf(x-1,y-1);
end
end
figure();
subplot(1,2,1);
imshow(g_hmf,[0 1]);
xlabel('c).Ruselt of Denoise by Hmf');
逆谐波均值滤波器恢复图像
f = imread('E:\课程\数字图像处理\作业\第六次作业\lena加入椒盐噪声后.bmp');
f = mat2gray(f,[0 255]);
[M,N] = size(f);
g_Ex = zeros(M+2,N+2);
for x = 1:M
g_Ex(x+1,:) = [f(x,1) f(x,:) f(x,N)];
end
g_Ex(1,:) = g_Ex(2,:);
g_Ex(M+2,:) = g_Ex(M+1,:);
Q = -1.5;
g_cmf = zeros(M,N);
for x = 2:M+1
for y = 2:N+1
g_cmf_M = (g_Ex(x ,y))^(1+Q);
g_cmf_M = g_cmf_M + (g_Ex(x-1,y))^(1+Q);
g_cmf_M = g_cmf_M + (g_Ex(x ,y-1))^(1+Q);
g_cmf_M = g_cmf_M + (g_Ex(x+1,y))^(1+Q);
g_cmf_M = g_cmf_M + (g_Ex( x,y+1))^(1+Q);
g_cmf_M = g_cmf_M + (g_Ex(x-1,y+1))^(1+Q);
g_cmf_M = g_cmf_M + (g_Ex(x+1,y-1))^(1+Q);
g_cmf_M = g_cmf_M + (g_Ex(x+1,y+1))^(1+Q);
g_cmf_M = g_cmf_M + (g_Ex(x-1,y-1))^(1+Q);
g_cmf_D = (g_Ex(x ,y))^(Q);
g_cmf_D = g_cmf_D + (g_Ex(x-1,y))^(Q);
g_cmf_D = g_cmf_D + (g_Ex(x ,y-1))^(Q);
g_cmf_D = g_cmf_D + (g_Ex(x+1,y))^(Q);
g_cmf_D = g_cmf_D + (g_Ex( x,y+1))^(Q);
g_cmf_D = g_cmf_D + (g_Ex(x-1,y+1))^(Q);
g_cmf_D = g_cmf_D + (g_Ex(x+1,y-1))^(Q);
g_cmf_D = g_cmf_D + (g_Ex(x+1,y+1))^(Q);
g_cmf_D = g_cmf_D + (g_Ex(x-1,y-1))^(Q);
g_cmf(x-1,y-1) = g_cmf_M/g_cmf_D;
end
end
figure();
subplot(1,2,1);
imshow(g_cmf,[0 1]);
xlabel('b).Ruselt of Denoise by Cmf(Q=-1.5)');
2、
添加椒盐噪声
I=imread(' E:\课程\数字图像处理\作业\第六次作业\lena.bmp');
figure(1);
imshow(I);
title('源图像lena.bmp');
imwrite(I,'lena原始图像.bmp');
I2=imnoise(I,'salt & pepper',0.1);
figure(2);
imshow(I2);
title('加入椒盐噪声后的lena.bmp');
imwrite(I2,'lena加入椒盐噪声后.bmp');
3、
运动模糊滤波器
I=imread(' E:\课程\数字图像处理\作业\第六次作业\lena.bmp ');
figure(1);
imshow(I);
title('lena.bmp原始图像');
imwrite(I,'lena原始图像.bmp');
f=double(I);
F=fft2(f);
F=fftshift(F);
[M,N]=size(F);
a=0.1;b=0.1;T=1;
for u=1:M
for v=1:N H(u,v)=(T/(pi*(u*a+v*b)))*sin(pi*(u*a+v*b))*exp(-sqrt(-1)*pi*(u*a+v*b));
G(u,v)=H(u,v)*F(u,v);
end
end
G=ifftshift(G);
g=ifft2(G);
g=256.*g./max(max(g));
g=uint8(real(g));
figure(2);
imshow(g);
title('运动模糊化lena.bmp');
imwrite(g,'lena运动模糊的结果.bmp');
维纳滤波器自编版
I=imread(' E:\课程\数字图像处理\作业\第六次作业\lena运动模糊+高斯噪声.bmp');
figure(1);
imshow(I);
title('lena运动模糊+高斯噪声');
imwrite(I,'lena运动模糊+高斯噪声.bmp');
g=double(I);
G=fft2(g);
G=fftshift(G);
[M,N]=size(G);
a=0.1;b=0.1;T=1;K=0.0259;
for u=1:M
for v=1:N H(u,v)=(T/(pi*(u*a+v*b)))*sin(pi*(u*a+v*b))*exp(-sqrt(-1)*pi*(u*a+v*b));
F(u,v)=1/H(u,v)*(abs(H(u,v)))^2/((abs(H(u,v)))^2+K)*G(u,v);
end
end
F=ifftshift(F);
f=ifft2(F);
f=256.*f./max(max(f));
f=uint8(real(f));
figure(2);
imshow(f);
title('维纳滤波的将结果');
imwrite(f,'维纳滤波的结果(K=0.0259).bmp');
维纳滤波器寻找信噪比最大的K值
I=imread(' E:\课程\数字图像处理\作业\第六次作业\lena运动模糊+高斯噪声.bmp');
figure(1);
imshow(I);
title('lena运动模糊+高斯噪声');
imwrite(I,'lena运动模糊+高斯噪声.bmp');
g=double(I);
G=fft2(g);
G=fftshift(G);
[M,N]=size(G);
a=0.1;b=0.1;T=1;i=1;
format long
for k=0.01:0.01:0.11
for u=1:M
for v=1:N H(u,v)=(T/(pi*(u*a+v*b)))*sin(pi*(u*a+v*b))*exp(-sqrt(-1)*pi*(u*a+v*b)); F(u,v)=(1/H(u,v))*((abs(H(u,v)))^2/((abs(H(u,v)))^2+k))*G(u,v);
end
end
F=ifftshift(F);
f=ifft2(F);
f=256.*f./max(max(f));
f=uint8(real(f));
figure;
imshow(f);
title('维纳滤波的结果');
e=f-uint8(g);
SNR(i)=sum(sum(g.^2))/sum(sum(e.^2));
i=i+1;
end
idx=find(max(SNR))
维纳滤波器MATLAB版
I=imread(' E:\课程\数字图像处理\作业\第六次作业\lena.bmp');
H=fspecial('motion',50,45);
I1=imfilter(I,H,'circular','conv');
figure(1);
imshow(I1);
title('运动模糊后的lena.bmp(角度为45度)');
imwrite(I1,'lena运动模糊（调用matlab中的函数）.bmp');
I2=imnoise(I1,'gaussian',0,0.01);
figure(2)
imshow(I2);
title('加噪并模糊的lena.bmp');
imwrite(I2,'lena运动模糊+高斯噪声（调用matlab中的函数0.bmp');
figure(3);
noise=imnoise(zeros(size(I)),'gaussian',0,0.01);
NSR=sum(noise(:).^2)/sum(im2double(I(:)).^2);
I3=deconvwnr(I2,H,NSR);
imshow(I3);
title('维纳滤波的结果');
imwrite(I3,'lena维纳滤波的结果（调用MATLAB中的函数）.bmp');
约束最小二乘滤波（MATLAB版）
I=imread(' E:\课程\数字图像处理\作业\第六次作业\lena.bmp');
h=fspecial('motion',50,45);
I1=imfilter(I,h,'circular','conv');
I2=imnoise(I1,'gaussian',0,0.01);
figure(1);
imshow(I2);
title('lena运动模糊+高斯噪声');
imwrite(I2,'lena运动模糊+高斯噪声（MATLAB版）.bmp');
V=0.0001;
NoisePower=V*prod(size(I));
[g,LAGRA]=deconvreg(I1,h,NoisePower);
figure(2);
imshow(g);
title('约束最小二乘滤波的结果（MATLAB版）');
imwrite(g,'约束最小二乘滤波的结果（MATLAB版）.bmp');