matlab图像复原(逆滤波、维纳滤波等)

clear; I=imread('1.jpg'); I=rgb2gray(I); figure; subplot(2,3,1);imshow(I);title('原图像'); [m,n]=size(I); F=fftshift(fft2(I)); k=0.0025; for u=1:m %退化模型 for v=1:n H(u,v)=exp((-k)*(((u-m/2)^2+(v-n/2)^2)^(5/6))); end end G0=F.*H; I0=real(ifft2(fftshift(G0))); noise=imnoise(zeros(size(I0)),'gaussian',0,0.001) ; I1=I0+noise; subplot(2,3,3);imshow(uint8(I1));title('模糊退化且添加高斯噪声的图像'); %逆滤波复原 G=fftshift(fft2(I1)); %傅里叶变换 F1=G./H; I2=ifft2(fftshift(F1)); subplot(2,3,4);imshow(uint8(I2));title('逆滤波复原图'); %维纳滤波复原 K=0.1; for u=1:m for v=1:n H(u,v)=exp(-k*(((u-m/2)^2+(v-n/2)^2)^(5/6))); H0(u,v)=(abs(H(u,v)))^2; H1(u,v)=H0(u,v)/(H(u,v)*(H0(u,v)+K)); end end F2=H1.*G; I3=ifft2(fftshift(F2)); subplot(2,3,5);imshow(uint8(I3));title('维纳滤波复原图'); %最小二乘方滤波 X=[0,-1,0;-1,4,-1;0,-1,0]; L=zeros(m,n); for i=1:3 for j=1:3 L(i,j)=X(i,j); end end for u=1:m for v=1:n P(u,v)=fftshift(fft2(L(u,v)));%拉布拉斯算子的傅里叶变化 end end s=0.1; for u=1:m for v=1:n H(u,v)=exp(-k*(((u-m/2)^2+(v-n/2)^2)^(5/6))); H0(u,v)=(abs(H(u,v)))^2; P0(u,v)=(abs(P(u,v)))^2; H2(u,v)=conj(H(u,v));%共轭 H1(u,v)=H2(u,v)/(H0(u,v)+P0(u,v)*s); end end F3=H1.*G; I4=ifft2(fftshift(F3)); subplot(236);imshow(uint8(I4));title('最小二乘方滤波复原图');