图像处理上机作业题.doc

2、用Matlab语言编写直方图规定化程序clc;I=imread(tire.tif);J=histeq(I,32);counts,x=imhist(J);Q=imread(eight.tif);figure;imshow(Q);figure;imhist(Q);M=histeq(Q,counts);figure;imshow(M);figure;imhist(M);3、编写程序实现半邻域选择平均法进行空间域图像平滑的程序。clear;clc;I=imread(pout.tif);J=imnoise(I,salt & pepper,0.02);K=filter2(fspecial(average,3),J)/255;subplot(131),imshow(I);subplot(132),imshow(J);subplot(133),imshow(K);4、针对某种平滑方法，比较对高斯噪声，椒盐噪声和乘法性噪声的去噪效果% 使用二维中值滤波对噪声进行处理clc;%原始图像以及加上噪声后的图像I,map=imread(pout.tif);imshow(I,map);noisy1=imnoise(I , gaussian , 0.05);%加入0.05的高斯白噪声noisy2=imnoise(I , salt & pepper , 0.05);%加入0.05的椒盐噪声noisy3=imnoise(I , speckle , 0.05);%加入0.05的乘法性噪声figure;imshow(noisy1,map);figure;imshow(noisy2,map);figure;imshow(noisy3,map);%二维窗口越大图像细节丢失越多% 3*3窗口的二维中值滤波hood=3;filtered1=medfilt2(noisy1,hood hood);filtered2=medfilt2(noisy2,hood hood);filtered3=medfilt2(noisy3,hood hood);figure;imshow(filtered1);figure;imshow(filtered2);figure;imshow(filtered3);原图像加噪声后去噪声后5、编写掩模匹配法实现锐化的程序，对同一图像比较Robison，Prewitt及Kirsch模板的锐化效果% Robisonclc;I,map=imread(pout.tif);imshow(I,map);M N=size(I);%myfilt1= 1 2 1; 0 0 0; -1 -2 -1;filtered1=filter2(myfilt1,I);%myfilt2= 2 1 0; 1 0 -1; 0 -1 -2;filtered2=filter2(myfilt2,I);%myfilt3= 1 0 -1; 2 0 -2; 1 0 -1;filtered3=filter2(myfilt3,I);%myfilt4= 0 -1 -2; 1 0 -1; 2 1 0;filtered4=filter2(myfilt4,I);%myfilt5=-1 -2 -1; 0 0 0; 1 2 1;filtered5=filter2(myfilt5,I);%myfilt6=-2 -1 0; -1 0 -1; 0 1 2;filtered6=filter2(myfilt6,I);%myfilt7=-1 0 1; -2 0 2; -1 0 1;filtered7=filter2(myfilt7,I);%myfilt8= 0 1 2; -1 0 1; -2 -1 0;filtered8=filter2(myfilt8,I);%for i=1:M for j=1:N x=filtered1(i,j),filtered2(i,j),filtered3(i,j),filtered4(i,j),. filtered5(i,j),filtered6(i,j),filtered7(i,j),filtered8(i,j); OUT(i,j)=max(x); endendfigure;imshow(OUT,map);% Prewittclc;%I,map=imread(pout.tif);imshow(I,map);M N=size(I);%myfilt1=1 1 1;1 -2 1;-1 -1 -1;filtered1=filter2(myfilt1,I);myfilt2=1 1 1;1 -2 -1;1 -1 -1;filtered2=filter2(myfilt2,I);myfilt3=1 1 -1;1 -2 -2;1 1 -1;filtered3=filter2(myfilt3,I);myfilt4=1 -1 1;1 -2 -1;1 1 1;filtered4=filter2(myfilt4,I);myfilt5=-1 -1 -1;1 -2 1;1 1 1;filtered5=filter2(myfilt5,I);myfilt6=-1 -1 1;-1 -2 1;1 1 1;filtered6=filter2(myfilt6,I);myfilt7=-1 0 1;-1 -2 1;-1 1 1;filtered7=filter2(myfilt7,I);myfilt8=1 1 1;-1 -2 1;-1 -1 1;filtered8=filter2(myfilt8,I);%for i=1:M for j=1:N x=filtered1(i,j),filtered2(i,j),filtered3(i,j),filtered4(i,j),. filtered5(i,j),filtered6(i,j),filtered7(i,j),filtered8(i,j); OUT(i,j)=max(x); endend%figure;imshow(OUT,map);% Kirschclc;I,map=imread(pout.tif);imshow(I,map);M N=size(I);%myfilt1=5 5 5;-3 0 -3;-3 -3 -3;filtered1=filter2(myfilt1,I);myfilt2=5 5 -3;5 0 -3;-3 -3 -3;filtered2=filter2(myfilt2,I);myfilt3=5 -3 -3;5 0 -3;5 -3 -3;filtered3=filter2(myfilt3,I);myfilt4=-3 -3 -3;5 0 -3;5 5 -3;filtered4=filter2(myfilt4,I);myfilt5=-3 -3 -3;-3 0 -3;5 5 5;filtered5=filter2(myfilt5,I);myfilt6=-3 -3 -3;-3 0 5;-3 5 5;filtered6=filter2(myfilt6,I);myfilt7=-3 -3 5;-3 0 5;-3 -3 5;filtered7=filter2(myfilt7,I);myfilt8=-3 5 5;-3 0 5;-3 -3 3;filtered8=filter2(myfilt8,I);%for i=1:M for j=1:N x=filtered1(i,j),filtered2(i,j),filtered3(i,j),filtered4(i,j),. filtered5(i,j),filtered6(i,j),filtered7(i,j),filtered8(i,j); OUT(i,j)=max(x); endend%figure;imshow(OUT,map);6、用Matlab编写出图像高通滤波实现锐化的程序，并通过实例进行各种高通滤波器性能的比较% clc;I,map=imread(pout.tif);figure;imshow(I,map);noisy=imnoise(I,gaussian, 0.05);figure;imshow(noisy,map);M N=size(I);F=fft2(double(noisy);fftshift(F);Dcut=100;D0=150;D1=250;for u=1:M for v=1:N D(u,v)=sqrt(u2+v2); BUTTERH(u,v)=1/(1+(sqrt(2)-1)*(Dcut/D(u,v)2); EXPOTH(u,v)=exp(log(1/sqrt(2)*(Dcut/D(u,v)2); if D(u,v)=200 IDEALH(u,v)=1; else IDEALH(u,v)=0; end if D(u,v)=D1 TRAPEH(u,v)=0; elseif D(u,v)=D0; TRAPEH(u,v)=(D(u,v)-D1)/(D0-D1); else TRAPEH(u,v)=1; end endendIDEALG=IDEALH.*F;IDEALfiltered=ifft2(IDEALG);BUTTERG=BUTTERH.*F;BUTTERfiltered=ifft2(BUTTERG);EXPOTG=EXPLOTH.*F;EXPOTfiltered=ifft2(EXPOTG);TRAPEG=TRAPEH.*F;TRAPEfiltered=ifft2(TRAPEG);figure;imshow(IDEALfiltered,map)figure;imshow(BUTTERfiltered,map)figure;imshow(EXPOTfiltered,map) figure;imshow(TRAPEfiltered,map) 1、Matlab编程实现点模板，线模板检测图像区域应用点模板的初始灰度级分布如下图，输出点模板匹配后的灰度分布：1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 1 1 1 1 1 11 1 1 4 4 4 4 4 1 1 1 11 1 1 4 4 4 4 4 4 1 1 11 1 1 1 4 4 4 4 4 1 1 11 1 1 1 4 4 4 4 1 1 1 11 1 1 1 1 4 4 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1data=1,1,1,1,1,1,1,1,1,1,1,1; 1,1,1,1,1,4,1,1,1,1,1,1; 1,1,1,4,4,4,4,4,1,1,1,1; 1,1,1,4,4,4,4,4,4,1,1,1; 1,1,1,1,4,4,4,4,4,1,1,1; 1,1,1,1,4,4,4,4,1,1,1,1; 1,1,1,1,1,4,4,1,1,1,1,1,; 1,1,1,1,1,1,1,1,1,1,1,1;n,m=size(data);disp(初始灰度级分布);datadm=ones(n,m); myfit=-1 -1 -1;-1 8 -1;-1 -1 -1;for i=1:n-2 for j=1:m-2 dm(i+1,j+1)=sum(sum(myfit.*data(i:i+2,j:j+2); endend disp(点模板匹配后的灰度分布); n,m=find(dm=1); data(n,m)=dm(n,m); data(data0)=0; data初始灰度级分布data = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 1 1 1 1 1 1 1 1 1 4 4 4 4 4 1 1 1 1 1 1 1 4 4 4 4 4 4 1 1 1 1 1 1 1 4 4 4 4 4 1 1 1 1 1 1 1 4 4 4 4 1 1 1 1 1 1 1 1 1 4 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1点模板匹配后的灰度分布data = 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 15 0 0 0 0 0 1 1 0 0 15 6 6 6 12 0 0 0 1 1 0 0 12 3 0 0 3 12 0 0 1 1 0 0 0 6 0 0 3 12 0 0 1 1 0 0 0 12 3 3 9 0 0 0 1 1 0 0 0 0 12 12 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1应用线模板的初始灰度级分布如下图，输出线模板匹配后的灰度分布：1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 2 1 1 1 1 1 2 1 1 1 1 1 11 1 2 1 1 1 1 2 1 1 1 2 2 2 11 1 2 1 1 1 2 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1Grey=textread (D:我的文档桌面初始图像灰度分布.txt);n,m=size (Grey);k=max (n,m)/min(n,m);Lmould_ 00=-1 -1 -1;2 2 2;-1 -1 -1;Lmould_ 45=-1 -1 2;-1 2 -1;2 -1 -1;Lmould_ 90=-1 2 -1;-1 2 -1;-1 2 -1;Lmould_ 135=2 -1 -1;-1 2 -1;-1 -1 2;Grey_Leaf=ones (n,n,k);Grey_temp=ones (n,n);account=1;for t=1:k Grey_Leaf (:,:,t)=Grey (:,account:account+n-1); account=account+n;end for t=1:k for i=1:n-2 for j=1:n-2 a1=sum (sum (Lmould_ 00.*Grey_Leaf (i:i+2,j:j+2,t); a2=sum (sum (Lmould_ 45.*Grey_Leaf (i:i+2,j:j+2,t); a3=sum (sum (Lmould_ 90.*Grey_Leaf (i:i+2,j:j+2,t); a4=sum (sum (Lmould_ 135.*Grey_Leaf (i:i+2,j:j+2,t); Grey_temp (i+1,j+1)=max (a1,a2,a3,a4); end end n1,m1=find (Grey_temp=1); Grey_Leaf (n1,m1,t)=Grey_temp (n1,m1); Grey_temp (n1,m1)=1; end disp (初始灰度级分布); Grey_Leafdisp (线模板边缘检测结果); Grey_Leaf2、用Mathematical求解一次平面拟合中a,b,c三个参量。即给出书中P115公式4.1.31, 4.1.32, 4.1.33的求解过程。ClearAllGlobal*gx_,y_:=a x+b y+c;fx_,y_:=Mx,y;Mx,y:=Ax;y(*矩阵元素*)Gx_,y_:=(gx,y-fx,y)2;Epsilon=Gm,n+Gm+1,n+Gm,n+1+Gm+1,n+1;DJ=SolveDEpsilon,a=0&DEpsilon,b=0&DEpsilon,c=0,a,b,c;aa=DJ1,1,2bb=DJ1,2,2cc=DJ1,3,2结果：a=1/2 (-Mm,n-Mm,1+n+M1+m,n+M1+m,1+n)b=1/2 (-Mm,n+Mm,1+n-M1+m,n+M1+m,1+n)c=1/4 (3 Mm,n+2 m Mm,n+2 n Mm,n+Mm,1+n+2 m Mm,1+n-2 n Mm,1+n+M1+m,n-2 m M1+m,n+2 n M1+m,n-M1+m,1+n-2 m M1+m,1+n-2 n M1+m,1+n)3、编程实现灰度差判别准则的合并法形成区域的过程。假设原图像矩阵为2 1 1 2 1 11 5 4 5 5 12 5 8 9 6 21 6 9 8 6 11 6 8 9 8 11 2 1 1 2 1设T=2编程实现其最终区域分割的结果，基