图像处理第一次作业.docx

1.绘制出一幅2M×2N的灰度图像直方图：对该图像进行均衡化处理，绘制出均衡后图像的直方图：对该图像进行灰度变换，使变换后的图像大致如下图所示的归一化直方图，并绘制出变换后实际的直方图。(1)程序clear all;%直方图均衡化A = imread(lena.jpg,jpg);B = uint8(A(:,:,1)*0.3+A(:,:,2)*0.59+A(:,:,3)*0.11);%B=rgb2gray(A); [m,n] = size(B);%测量图像尺寸参数,m*n=490*490B1=zeros(1,256);for k=0:255 B1(k+1)=length(find(B==k));endB2=zeros(1,256);for i=1:256 for j=1:i B2(i)=B2(i)+B1(j); endendB3=round(double(255*B2)/(m*n));B4=B;for i=0:255 B4(find(B==i))=B3(i+1);endfor k=0:255 B5(k+1)=length(find(B4==k));endfigure;subplot(2,2,1);bar(0:255,B1,b);title(原图像直方图);subplot(2,2,2);bar(0:255,B5,b);title(均衡后的直方图);subplot(2,2,3);B6=histeq(B);dif=B6-B4;imshow(dif);title(matlab函数均衡化减去自己编写函数均衡化);%直方图规定化C1=zeros(1,256);x=0:255;for i=1:128 C1(i)=1/(127*128)*(128-i); C1(257-i)=C1(i);endfigure;stem(x,C1,.);axis([0 255 0 max(C1(:))]);C2=zeros(1,256);for i=1:256 for j=1:i C2(i)=C2(i)+C1(j); endendC2=round(C2*255);D1=zeros(1,256); for i=1:256 for j=1:255 if B3(j)=C2(i)B3(j+1)=C2(i) if abs(B3(j)-C2(i))=abs(B3(j+1)-C2(i)) D1(i)=j; else D1(i)=j+1; end end end end D2=zeros(1,256); D2(1)=C1(1); for i=2:256 if D1(i-1)0 for k=(D1(i-1)+1):D1(i) D2(i)=D2(i)+B1(k); end else D2(i)=0; end end figure; bar(0:255,D2,b); title(规定化后的直方图)(2)实验结果2.分别在两幅灰度图像中加入一定量的高斯噪声和椒盐噪声，噪声强度自定，然后采用3×3的均值滤波器和3×3的中值滤波器分别对噪声图像进行处理，给出两种处理方法的峰值信噪比(PSNR)。(1)程序clear allA=imread(lena.jpg,jpg); B=uint8(A(:,:,1)*0.3+A(:,:,2)*0.59+A(:,:,3)*0.11);%B=rgb2gray(A);B_gau=imnoise(B,gaussian,0,0.002); %添加高斯噪声 B_salt=imnoise(B,salt pepper,0.02); %添加椒盐噪声fli_med_gau=medfilt2(B_gau);%中值滤波函数fli_med_salt=medfilt2(B_salt);h1=fspecial(average,3);%均值滤波函数fli_avg_gau=uint8(round(filter2(h1,B_gau)));h2=fspecial(average,3);%均值滤波函数fli_avg_salt=uint8(round(filter2(h2,B_salt)));[m,n] = size(B); %测量图像尺寸参数%实现中值滤波功能B_fli_med_gau = B_gau;%高斯噪声均值中值滤波pixel_block = zeros(1,9); %开辟一个一行九列的数组，均赋于初值0for i = 2:m-1 %各个像素点（不包括边界） for j = 2:n-1 pixel_block = reshape(B(i-1:i+1,j-1:j+1),9,1); %采用3*3的窗口，将一个像素点及其周围的8各点，依次存入新%开辟的数组pixel_block中 sorted_block = sort(pixel_block); %将数组pixel_block排序