數(shù)字圖像課程設(shè)計(jì)

1、均衡化： 圖像一直方圖集中在低灰度區(qū)，圖像偏暗；圖像二直方圖集中在高灰度區(qū)，圖像偏亮；圖像三直方圖集中在中部，圖像效反差小。a=imread('fig1_1_1.bmp');b=imread('fig1_1_2.bmp');c=imread('fig1_1_3.bmp');a1=histeq(a);b1=histeq(b);c1=histeq(c);subplot(3,3,1),imshow(a),title('原始圖像1');subplot(3,3,2),imshow(b),title('原始圖像2');subp

2、lot(3,3,3),imshow(c),title('原始圖像3');subplot(3,3,4),imhist(a),title('圖1直方圖');subplot(3,3,5),imhist(b),title('圖2直方圖');subplot(3,3,6),imhist(c),title('圖3直方圖');subplot(3,3,7),imshow(a1),title('均衡化后圖像1');subplot(3,3,8),imshow(b1),title('均衡化后圖像2');subplot(3,

3、3,9),imshow(c1),title('均衡化后圖像3');平滑濾波：a=imread('fig1_2.bmp ');a1=imnoise(a,'salt & pepper',0.02);l1=filter2(fspecial('average',3),a1)/255; l2=filter2(fspecial('average',5),a1)/255; l3=filter2(fspecial('average',7),a1)/255; l4=filter2(fspecial('

4、average',9),a1)/255; figure (1)subplot(2,3,1),imshow(a),title('原始圖像');subplot(2,3,2),imshow(l1);title('3*3 模板平滑濾波');subplot(2,3,3),imshow(l2);title('5*5 模板平滑濾波');subplot(2,3,4),imshow(l3);title('7*7 模板平滑濾波');subplot(2,3,5),imshow(l4);title('9*9 模板平滑濾波');l1

5、1=filter2(fspecial('average',3),a)/255; l22=filter2(fspecial('average',5),a)/255; l33=filter2(fspecial('average',7),a)/255; l44=filter2(fspecial('average',9),a)/255; figure (2)subplot(2,3,1),imshow(a),title('原始圖像');subplot(2,3,2),imshow(a1),title(' 添加椒鹽噪聲的

6、圖像');subplot(2,3,3),imshow(l11);title('3*3 模板平滑濾波');subplot(2,3,4),imshow(l22);title('5*5 模板平滑濾波');subplot(2,3,5),imshow(l33);title('7*7 模板平滑濾波');subplot(2,3,6),imshow(l44);title('9*9 模板平滑濾波¨');平滑濾波是低頻增強(qiáng)的空間域?yàn)V波技術(shù)。它的目的有兩類：一類是模糊；另一類是消除噪音?？臻g域的平滑濾波一般采用簡單平均法進(jìn)行，就是求鄰

7、近像元點(diǎn)的平均亮度值。在噪聲均勻分布在邊緣時(shí)，使用平滑濾波會有較大的誤差，如果噪聲不是均勻分布，可有效去除噪聲。鄰域的大小與平滑的效果直接相關(guān)，鄰域越大平滑的效果越好，但鄰域過大，平滑會使邊緣信息損失的越大，從而使輸出的圖像變得模糊，因此需合理選擇鄰域的大小。銳化濾波：i=imread('fig1_2.bmp'); f= 8 ;g=-1 -1 -1; -1 8 -1; -1 -1 -1; m=-1 -1 -1 -1 -1; -1 -1 8 -1 -1;-1 -1 -1 -1 -1;n=-1 -1 -1 -1 -1 -1 -1; -1 -1 -1 8 -1 -1 -1;-1 -1

8、 -1 -1 -1 -1 -1;p=-1 -1 -1 -1 -1 -1 -1 -1 -1;-1 -1 -1 -1 8 -1 -1 -1 -1;-1 -1 -1 -1 -1 -1 -1 -1 -1;h=double(i);e=conv2(h,f,'same');j=conv2(h,g,'same');x=conv2(h,m,'same');y=conv2(h,n,'same');z=conv2(h,n,'same');subplot(2,3,1);imshow(h);title('原始圖像');sub

9、plot(2,3,2);imshow(e);title('濾波后圖像1');subplot(2,3,3);imshow(j);title('濾波后圖像2');subplot(2,3,4);imshow(x);title('濾波后圖像3');subplot(2,3,5);imshow(y);title('濾波后圖像4'); subplot(2,3,6);imshow(z);title('濾波后圖像5');圖像銳化處理的目的是為了使圖像的邊緣、輪廓線以及圖像的細(xì)節(jié)變的清晰。一般情況下，在計(jì)算機(jī)圖像處理中可用微分運(yùn)算和高

10、通濾波器來實(shí)現(xiàn)圖像的銳化。空間域圖像的銳化常用的是拉普拉斯運(yùn)算，它不僅是偏導(dǎo)數(shù)的線性組合，而且是各向同性的，這樣可以使圖像中間任何方向伸展的邊緣和輪廓線變得清晰低通濾波：I=imread('fig1_2.bmp'); m n p=size(I);I=double(I);subplot(2,2,1);image(I);colormap(gray),title('原始圖');Noise=wgn(m,n,25);New = Noise + I;subplot(2,2,2);image(New),title('噪聲圖');g=fft2(New); g=f

11、ftshift(g);M,N=size(g);nn=2; d0=20; m=fix(M/2); n=fix(N/2);for i=1:M for j=1:N d=sqrt(i-m)2+(j-n)2); h=1/(1+0.414*(d/d0)(2*nn); result(i,j)=h*g(i,j); endendresult=ifftshift(result);J2=ifft2(result); J3=uint8(real(J2);subplot(2,2,3);image(J3),title('截止頻率20 '); nn=2; d0=100; m=fix(M/2); n=fix(

12、N/2);for i=1:M for j=1:N d=sqrt(i-m)2+(j-n)2); h=1/(1+0.414*(d/d0)(2*nn); result(i,j)=h*g(i,j); endendresult=ifftshift(result);J2=ifft2(result);J3=uint8(real(J2);subplot(2,2,4);image(J3),title('截止頻率100 ')去掉信號中不必要的高頻成分，降低采樣頻率，避免頻率混淆，去掉高頻干擾高通濾波：I=imread('fig1_2.bmp');m n p=size(I);I=do

13、uble(I);subplot(2,2,1),image(I),colormap(gray);,title('原始圖');Noise=wgn(m,n,25);New = Noise + I;subplot(2,2,2);image(New),title('噪聲圖');g=fft2(New); g=fftshift(g);M,N=size(g);d0=15; m=fix(M/2); n=fix(N/2);for i=1:M for j=1:N d=sqrt(i-m)2+(j-n)2); if(d<=d0) h=0; else h=1; end result(

14、i,j)=h*g(i,j); endendresult=ifftshift(result);J1=ifft2(result);J2=uint8(real(J1);subplot(2,2,3),imshow(J2),title('IHPF濾波（d0=15)') ;M,N=size(g);d0=100; m=fix(M/2); n=fix(N/2);for i=1:M for j=1:N d=sqrt(i-m)2+(j-n)2); if(d<=d0) h=0; else h=1; end result(i,j)=h*g(i,j); endendresult=ifftshift

15、(result);J1=ifft2(result);J2=uint8(real(J1);subplot(2,2,4),imshow(J2),title('IHPF濾波（d0=100)') ;高通濾波器衰減或抑制低頻分量而通過高頻分量。圖像的邊緣、細(xì)節(jié)主要在高頻部分得到反映。而圖像的模糊是高頻部分較弱造成的。為了消除模糊，突出圖像的邊緣信息，則采用高通濾波器讓高頻部分通過，消弱圖像的低頻成分，再經(jīng)過傅里葉逆變換得到邊緣銳化的圖像。圖像還原：I=imread('fig1_3_1.bmp');subplot(2,2,1);imshow(I);title('模糊

16、圖像'); m,n=size(I); F=fftshift(fft2(I); k=0.0025; for u=1:m for v=1:n H(u,v)=exp(-k)*(u-m/2)2+(v-n/2)2)(5/6); end end G=F.*H; I0=real(ifft2(fftshift(G); I1=imnoise(uint8(I0),'gaussian',0,0.001) subplot(2,2,2)，imshow(uint8(I1)，title('模糊退化且添加高斯噪聲的圖像'); F0=fftshift(fft2(I1); F1=F0./H

17、; I2=ifft2(fftshift(F1); subplot(2,2,3)，imshow(uint8(I2)，title('全逆濾波復(fù)原圖'); 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.*F0; I3=ifft2(fftshift(F2); subplot(2,2,4)，imshow(uint8(I3)，title('維納濾波復(fù)原圖

18、');形態(tài)學(xué)圖像處理：I1=imread('fig1_4_1.bmp'); I3=im2bw(I1); Subplot(221),imshow(I1) Subplot(222),imshow(I3) I4=bwmorph(I3,'open'); Subplot(223),imshow(I4) I5=bwmorph(I4,'close'); I=imread('fig1_4_2.bmp');B=strel('diamond',1);J1=imopen(I,B);J2=imclose(J1,B);subplot

19、(1,2,1);imshow(I);title('原始圖像');subplot(1,2,2);imshow(J2);title('濾噪結(jié)果');rgb=imread('fig1_5.bmp');rgb=im2double(rgb);r=rgb(:,:,1);g=rgb(:,:,2);b=rgb(:,:,3);subplot(3,3,1),imshow(rgb),title('原始圖像');subplot(3,3,4),imshow(r),title('原始圖像紅色分量');subplot(3,3,5),imshow

20、(g),title('原始圖像綠色分量');subplot(3,3,6),imshow(b),title('原始圖像藍(lán)色分量');num=0.5*(r-g)+(r-b);sym den;fen=sqrt(r-g).2+(r-g).*(g-b);theta=acos(num./(fen+eps);H=theta;H(b>g)=2*pi-H(b>g);H=H/(2*pi);num=min(min(r,g),b);fen=r+g+b;fen(fen=0)=eps;S=1-3.*num./fen;H(S=0)=0;I=(r+g+b)/3;hsi=cat(3,

21、H,S,I);subplot(3,3,7),imshow(H),title('H');subplot(3,3,8),imshow(S),title('S');subplot(3,3,9),imshow(I),title('I');I1=rgb2gray(rgb);subplot(3,3,2),imshow(I1);title('灰度圖像');k1=filter2(fspecial('average',3),I1)/255; subplot(3,3,3),imshow(k1);title('平滑濾波圖像

22、9;);圖像的校正：A=imread('fig3_1.bmp'); se=strel('ball',12,0);BW=imdilate(A,se);´BW2 = bwmorph(BW, 'thin',Inf);H,T,R = hough(BW2);P = houghpeaks(H,5); lines = houghlines(BW2,T,R,P);for k = 1:length(lines) xy = lines(k).point1; lines(k).point2;endm=(xy(2,2)- xy(1,2)/(xy(2,1)-xy

23、(1,1);M=atan(m);M=M*180/3.14;C=imrotate(A,M); subplot(1,2,1);imshow(A);title('原圖像');subplot(1,2,2);imshow(C);title('校正后的圖像');圖像融合：X1=imread('fig4_1_a.jpg');X2=imread('fig4_1_b.jpg');X3=imread('fig4_1_c.jpg');X4=imread('fig4_1_d.jpg');X5=imread('fig

24、4_1_e.jpg');X6=imread('fig4_1_f.jpg');subplot(3,2,1);imshow(X1);title('序列圖像1');subplot(3,2,2);imshow(X2);title('序列圖像2');subplot(3,2,3);imshow(X3);title('序列圖像3');subplot(3,2,4);imshow(X4);title('序列圖像4');subplot(3,2,5);imshow(X5);title('序列圖像5');subplot(3,2,6);imshow(X6);title('序列圖像');X1=double(X1);X2=double(X2);X3=double(X3);X4=double(X4);X5=double(X5);X6=double(X6);c1,I1=wavedec2(X1,3,'sym4');c2,I2=wavedec2(X2,3,'sym4');c3,I3=wavedec2(X3,3,'sym4');c4,I4=wavedec2(X4,3,'sym4