图像处理计算机作业.doc

第一章 上机作业使用函数读取图像lena512_gray.bmp，存取到矩阵中clear;A=imread(lena512_gray.bmp);B=imread(lena512_color.bmp);C=rgb2gray(B);figure(1);subplot(1,2,1);imshow(A);title(读取灰度图像);subplot(1,2,2);imshow(B);title(读取彩色图像);figure(2);subplot(1,2,1);imshow(B);title(原彩色图像);subplot(1,2,2);imshow(C);title(把彩色图像转换为灰度图像);for i=1:256 for j=1:256 D(i,j)=A(i,j); endendfigure(3);subplot(1,2,1);imshow(A);title(显示原图像);subplot(1,2,2);imshow(D);title(显示图像的局域部分);第二章 计算机作业(1) 图像的采样%下采样A=imread(barb1.bmp);m,n=size(A);B=zeros(m/2,n);for i=1:m/2 B(i,1:end)=A(2*i,1:end);endC=zeros(m/2,n/2);for j=1:n/2C(1:end,j)= uint8(B(1:end,2*j);endD = uint8(C);imshow(D);%下采样A=imread(barb1.bmp);m,n=size(A);B=zeros(m/2,n);for i=1:m/2 B(i,1:end)=A(2*i,1:end);endC=zeros(m/2,n/2);for j=1:n/2C(1:end,j)= uint8(B(1:end,2*j);endD = uint8(C);subplot(1,2,1);imshow(D);%上采样m,n=size(D);F=zeros(2*m,n);for i=1:2*m-1 F(i,1:end)=D(floor(i/2)+1,1:end);endG=zeros(2*m,2*n);for j=1:2*n-1G(1:end,j)= uint8(F(1:end,floor(j/2)+1);endE = uint8(G);subplot(1,2,2);imshow(E); B=imresize(A,row/2,column/2); imshow(B); A=imread(barb1.bmp); row,column=size(A); B=imresize(A,row/2,column/2); imshow(B);（2） G=im2bw(A,0.4); imshow(G); G=im2bw(A,0.48); imshow(G); G=im2bw(A,0.5); imshow(G); G=im2bw(A,0.55); imshow(G);对比可知 阈值设置在0.48左右比较合适。(3) A=imread(barb2.bmp); noise=uint8(randn(size(A); B=A+noise; imshow(B);第三章 上机作业(1) 图像的直方图I=imread(malight.bmp);m1=zeros(1,256);m,n=size(I);for i=1:256for j=1:256 m1(I(i,j)=m1(I(i,j)+1;endendx=1:256;y=m1;subplot(1,2,1);imshow(I);title(原图像);subplot(1,2,2);bar(x,y,g);title(直方图);调用imhist的效果更好一些。(2) 图像的直方图均衡I=imread(malight.bmp);m1=zeros(1,256);m,n=size(I);for i=1:256for j=1:256 m1(I(i,j)=m1(I(i,j)+1;endendm2=zeros(1,256);for i=1:256 m2(i)=m1(i)./(m*n);endm3=zeros(1,256);for i=1:256 for j=1:i m3(i)=floor(m2(j)*255+0.5)+m3(i); endendm4=zeros(m,n);for i=1:m for j=1:n m4(i,j)=m3(I(i,j)+1); endendI=uint8(I);m4=uint8(m4);subplot(2,2,1);imshow(I);title(原图像);x=1:256;y=m1(x);subplot(2,2,2);bar(x,y,r);%imhist(I);title(原图像的直方图);%axis(0,256,0,10000);subplot(2,2,3);imshow(m4);title(直方图均衡后的图像);y=m3(x);subplot(2,2,4);bar(x,y,r);%imhist(m4);title(直方图均衡后的直方图);%axis(0,200,0,10000);。(3) 图像降噪实验A=imread(malight.bmp);subplot(2,2,1);imshow(A);title(原图像);B=imnoise(A,salt & pepper,.2);subplot(2,2,2);imshow(B);title(加入椒盐噪声的图像);C=medfilt2(B,3,3);subplot(2,2,3);imshow(C);title(进行3X3中值滤波后的图像);D=medfilt2(B,7,7);subplot(2,2,4);imshow(D);title(进行7X7中值滤波后的图像);自己编的中值滤波器函数A=imread(malight.bmp);subplot(2,2,1);imshow(A);title(原图像);n=rand(size(A)*255;B=imnoise(A,salt & pepper,.2);c=(B=0)|(B=255);z=c.*n+(1-c).*double(A);D=z/255;subplot(2,2,2);imshow(D);title(加入噪声后的图像);n2=input(请输入中值滤波的模板的大小n);Y4=midfilt(z,n2); subplot(2,2,3);imshow(Y4);title(用自己编写的函数进行中值滤波之后的结果);%滤波函数：function d=midfilt(x,n);row,cloumn=size(x); x1=double(x);x2=x1;for i=1:row-n+1 for j=1:cloumn-n+1 C=x1(i:i+(n-1),j:j+(n-1);e=reshape(C,1,n*n);mm=median(e);x2(i+(n-1)/2,j+(n-1)/2)=mm; endendd=uint8(x2);输入n2=15的结果：第四章上机作业A = imread(Lena512_gray.bmp);figure,imshow(A),title(原图像);k=fspecial(sobel) ; % 定义滤波算子kY=filter2(k,A); figure,imshow(Y),title(sobel滤波);。图像模板匹配rgb_image=imread(A.jpg);gray_image=rgb2gray(rgb_image);image=double(gray_image);ed_image=image;rgb_back=imread(B.jpg);gray_back=rgb2gray(rgb_back);back=double(gray_back);x1,y1=size(image);%原图的大小x2,y2=size(back);%模板图像大小(模板要小些）for i=1:(x1-x2+1) for j=1:(y1-y2+1) I=image(i:(i+x2-1),j:(j+y2-1); T=back(1:x2,1:y2); aver_I=mean(mean(I); aver_T=mean(mean(T); sub_I=I(1:x2,1:y2)-aver_I; squ_sub_I=sub_I.*sub_I; sum_sub_I=sum(sum(sub_I); sum_squ_sub_I=sum(sum(squ_sub_I); sub_T=T(1:x2,1:y2)-aver_T; squ_sub_T=sub_T.*sub_T; sum_sub_T=sum(sum(sub_T); sum_squ_sub_T=sum(sum(squ_sub_T); mul=sub_I.*sub_T; sum_mul=sum(sum(mul); R=sum_mul/(sqrt(sum_squ_sub_I)*(sqrt(sum_squ_sub_T); if R0.9 %image(i:(i+x2-1),j:(j+y2-1)=255; m=i; n=j; image(i,j:(j+y2-1)=255; image(i+x2-1),j:(j+y2-1)=255; image(i:(i+x2-1),j)=255; image(i:(i+x2-1),(j+y2-1)=255; end endendimage(m:m+x2,n)=255;image(m:m+x2,n+y2)=255;image(m,n:n+y2)=255;image(m+x2,n:n+y2)=255;h=mat2gray(image);figure,imshow(h);ed1=edge(ed_image,canny,0.08); ed2=edge(ed_image,prewitt,14);ed=ed1&ed2;figure,imshow(ed);figure,imshow(ed1);figure,imshow(ed2);+=放大后图形如上图所示。第四章 上机作业1 图像降噪实验。im_original=imread(barb1.bmp);im_restored=imread(barb1n1.bmp);psnr = PSNR(im_original, im_restored);psnrn1=psnr;im_original=imread(barb1.bmp);im_restored=imread(barb1n2.bmp);psnr = PSNR(im_original, im_restored);psnrn2=psnr;g=imread(barb1.bmp);g1=g;m n=size(g);G,fx,fy=FFT2D(g,m,n,1);G1=G;g=imread(barb1n1.bmp);gn1=g;m n=size(g);G,fx,fy=FFT2D(g,m,n,1);G2=G;GD=abs(G2-G1);figuresurf(fx,fy,abs(GD)camlight left; lighting phong%colormap(glue)shading interpylabel(fy (cyc/m); xlabel(fx (cyc/m);title(n1与原图像的频谱差值频谱);g=imread(barb1n2.bmp);gn2=g;m n=size(g);G,fx,fy=FFT2D(g,m,n,1);G3=G;GD2=abs(G3-G1);figuresurf(fx,fy,abs(GD2)camlight left; lighting phong%colormap(glue)shading interpylabel(fy (cyc/m); xlabel(fx (cyc/m);title(n2与原图像的频谱差值频谱); h = fspecial(gaussian,3,0.66);B= imfilter(gn1,h,replicate);figure;subplot(2,2,1);imshow(g1);title(原图像);subplot(2,2,2);imshow(gn1);title(有高频噪声的图像barb1n1);subplot(2,2,3);imshow(B);title(barb1n1图像经过高斯低通滤波后的图像);im_original=imread(barb1.bmp);im_restored=B;psnr = PSNR(im_original, im_restored);psnrn1_gauss=psnr;h = fspecial(gaussian,7,1);B= imfilter(gn2,h,replicate);figure;subplot(2,2,1);imshow(g1);title(原图像);subplot(2,2,2);imshow(gn2);title(有高频噪声的图像barb1n2);subplot(2,2,3);imshow(B);title(barb1n2图像经过高斯低通滤波后的图像);im_original=imread(barb1.bmp);im_restored=B;psnr = PSNR(im_original, im_restored);psnrn2_gauss=psnr;函数function psnr = PSNR(im_original, im_restored)：function psnr = PSNR(im_original, im_restored)im_original = double(im_original);im_restored = double(im_restored);psnr = 20*log10(max(im_original(:)/sqrt(mean2(im_restored-im_original).2);函数function G, fx, fy = FFT2D(g,L,H,bshow)：function G, fx, fy = FFT2D(g,L,H,bshow)if nargin 45);k1=idct2(J1)/255;Yasolv1=(sum(sum(abs(J)45)./(a*b);J2=J.*(abs(J)50);k2=idct2(J2)/255;Yasolv2=(sum(sum(abs(J)50)./(a*b);J3=J.*(abs(J)60);k3=idct2(J3)/255;Yasolv3=(sum(sum(abs(J)60)./(a*b);figure;imshow(I);title(有块效应的图像);figure;subplot(2,2,1);imshow(I);subplot(2,2,2);imshow(k1);subplot(2,2,3);imshow(k2);subplot(2,2,4);imshow(k3);imshow(k1);title(阈值设为45的IDCT变换后的图像);figure;imshow(k2);title(阈值设为50的IDCT变换后的图像);figure;imshow(k3);title(阈值设为60的IDCT变换后的图像);im_restored=k1;psnr = PSNR(im_original, im_restored);psnrn_k1=psnr;im_restored=k2;psnr = PSNR(im_original, im_restored);psnrn_k2=psnr;im_restored=k3;psnr = PSNR(im_original, im_restored);psnrn_k3=psnr;函数：function psnr = PSNR(im_original, im_restored)function psnr = PSNR(im_original, im_restored)im_original = double(im_original);im_restored = double(im_restored);psnr = 20*log10(max(im_original(:)/sqrt(mean2(im_restored-im_original).2);分别设定三个阈值：45 50 60通过对比可以看出阈值设置在45左右 处理的效果最好。它们对应的信噪比为psnrn_k1 psnrn_k2 psnrn_k3psnrn_k1=5.3705 dBpsnrn_k2=5.3705 dBpsnrn_k3=5.3704 dB有块效应的图像的性噪比为psnrn_blockpsnrn_block=27.013 dB。3 图像 deblurring 实验g=imread(test_debluring.bmp);image=bldconv(g);figure;imshow(g);title(复原前的图像);im_original=g;im_restored=image;psnr = PSNR(im_original, im_restored);盲去卷积函数function image=bldconv(g)%Blind-deconv example%Created by realasking%Welcome to visit my blog: /realasking%finish time:5-22-2007a1=0.1;a2=0.01;PQ=paddedsize(size(g);G=fft2(g,PQ(1),PQ(2);y x=size(G);htemp=ones(3);h0=freqz2(htemp,PQ(1),PQ(2);R=Rcreat(y,x);H=h0;for k=1:10 %计算IMG和psfiMG=(conj(H).*G)./(c