数字图像课程设计

1、均衡化： 图像一直方图集中在低灰度区，图像偏暗；图像二直方图集中在高灰度区，图像偏亮；图像三直方图集中在中部，图像效反差小。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(原始图像1);subplot(3,3,2),imshow(b),title(原始图像2);subplot(3,3,3),imshow(c),title(原始图像3);subplot(3,3,4),i

2、mhist(a),title(图1直方图);subplot(3,3,5),imhist(b),title(图2直方图);subplot(3,3,6),imhist(c),title(图3直方图);subplot(3,3,7),imshow(a1),title(均衡化后图像1);subplot(3,3,8),imshow(b1),title(均衡化后图像2);subplot(3,3,9),imshow(c1),title(均衡化后图像3);平滑滤波：a=imread(fig1_2.bmp );a1=imnoise(a,salt & pepper,0.02);l1=filter2(fspecial

3、(average,3),a1)/255; l2=filter2(fspecial(average,5),a1)/255; l3=filter2(fspecial(average,7),a1)/255; l4=filter2(fspecial(average,9),a1)/255; figure (1)subplot(2,3,1),imshow(a),title(原始图像);subplot(2,3,2),imshow(l1);title(3*3 模板平滑滤波);subplot(2,3,3),imshow(l2);title(5*5 模板平滑滤波);subplot(2,3,4),imshow(l3

4、);title(7*7 模板平滑滤波);subplot(2,3,5),imshow(l4);title(9*9 模板平滑滤波);l11=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(原始图像);subplot(2,3,2),imshow(a1),tit

5、le( 添加椒盐噪声的图像);subplot(2,3,3),imshow(l11);title(3*3 模板平滑滤波);subplot(2,3,4),imshow(l22);title(5*5 模板平滑滤波);subplot(2,3,5),imshow(l33);title(7*7 模板平滑滤波);subplot(2,3,6),imshow(l44);title(9*9 模板平滑滤波);平滑滤波是低频增强的空间域滤波技术。它的目的有两类：一类是模糊；另一类是消除噪音。空间域的平滑滤波一般采用简单平均法进行，就是求邻近像元点的平均亮度值。在噪声均匀分布在边缘时，使用平滑滤波会有较大的误差，如果噪

6、声不是均匀分布，可有效去除噪声。邻域的大小与平滑的效果直接相关，邻域越大平滑的效果越好，但邻域过大，平滑会使边缘信息损失的越大，从而使输出的图像变得模糊，因此需合理选择邻域的大小。锐化滤波：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 -1 -1 -1 -1 -1;p=-1 -1 -1 -1 -1 -1 -1 -1 -1;-1 -1

7、 -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(原始图像);subplot(2,3,2);imshow(e);title(滤波后图像1);subplot(2,3,3);imshow(j);title(滤波后图像2);subplot(2,3,4);imshow(x);title(滤波后图

8、像3);subplot(2,3,5);imshow(y);title(滤波后图像4); subplot(2,3,6);imshow(z);title(滤波后图像5);图像锐化处理的目的是为了使图像的边缘、轮廓线以及图像的细节变的清晰。一般情况下，在计算机图像处理中可用微分运算和高通滤波器来实现图像的锐化。空间域图像的锐化常用的是拉普拉斯运算，它不仅是偏导数的线性组合，而且是各向同性的，这样可以使图像中间任何方向伸展的边缘和轮廓线变得清晰低通滤波：I=imread(fig1_2.bmp); m n p=size(I);I=double(I);subplot(2,2,1);image(I);col

9、ormap(gray),title(原始图);Noise=wgn(m,n,25);New = Noise + I;subplot(2,2,2);image(New),title(噪声图);g=fft2(New); g=fftshift(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(

10、result); J3=uint8(real(J2);subplot(2,2,3);image(J3),title(截止频率20 ); nn=2; 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); 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(截止频率100

11、 )去掉信号中不必要的高频成分，降低采样频率，避免频率混淆，去掉高频干扰高通滤波：I=imread(fig1_2.bmp);m n p=size(I);I=double(I);subplot(2,2,1),image(I),colormap(gray);,title(原始图);Noise=wgn(m,n,25);New = Noise + I;subplot(2,2,2);image(New),title(噪声图);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

12、=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(result);J1=ifft2(result);J2=uint8(real(J1);subplot(2,2,3),imshow(J2),title(IHPF滤波（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(dg)=2*pi-H(bg);H=H/(2*pi);nu

13、m=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,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(灰度图像);k1=filter2(fspecial(average,3),I1)/255; subplo

14、t(3,3,3),imshow(k1);title(平滑滤波图像);图像的校正：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(1,1);

15、M=atan(m);M=M*180/3.14;C=imrotate(A,M); subplot(1,2,1);imshow(A);title(原图像);subplot(1,2,2);imshow(C);title(校正后的图像);图像融合：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(fig4_1_e.jpg);X6=imread(fig4_1_f.jpg);subplot(3,2,1);imshow(X1);title(序列图像

16、1);subplot(3,2,2);imshow(X2);title(序列图像2);subplot(3,2,3);imshow(X3);title(序列图像3);subplot(3,2,4);imshow(X4);title(序列图像4);subplot(3,2,5);imshow(X5);title(序列图像5);subplot(3,2,6);imshow(X6);title(序列图像);X1=double(X1);X2=double(X2);X3=double(X3);X4=double(X4);X5=double(X5);X6=double(X6);c1,I1=wavedec2(X1,3

17、,sym4);c2,I2=wavedec2(X2,3,sym4);c3,I3=wavedec2(X3,3,sym4);c4,I4=wavedec2(X4,3,sym4);c5,I5=wavedec2(X5,3,sym4);c6,I6=wavedec2(X6,3,sym4);%对分解系数进行融合c=c1+c2+c3+c4+c5+c6;I=I1+I2+I3+I4+I5+I6; c=0.001*(c1+c2+c3+c4+c5+c6);%应用融合系数进行图像重构并显示XX=waverec2(c,I1,sym4);figure;XX=double(XX);imshow(XX);title(融合图像);图像压缩：clearI=imread(fig5_1.tif); %该图片在安装matlab的目录中找，原图为灰度图象 I=im2double(I);%图像存储类型转换 T=dctmtx(8);%离散余弦变换矩阵 B=blkproc(I,8 8,P1*x*P2,T,T); %对原图像进行DCT变换 mask=1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0