关于数字图像处理的基于matlab的

1、A=imread('abc.jpg');%图像的读取B=imshow(A);%显示图像Ax,map=imread('abc.jpg');%x是图像的数据矩阵，map是图像的颜色矩阵image（x）;colormap(map);C=size(x);%显示x矩阵的行数，列数，维数D=image(x);%显示图像AE=x(:,:,2);%显示想第二层二维矩阵（一般图像没rgb图像，第一层数据代表红的强度。第二层代表绿的强度。第三层代表蓝的强度）F=imfinio('abc.jpg');%图像文件信息的查询Example：加噪声I=imread('

2、;G:a.tif') ;p=rgb2gray(I);%显示灰度图J1=imnoise(p,'salt & pepper',0.04);subplot(121),subimage(J1);title('加入盐椒噪声后的图像，密度D=0.04');J2=imnoise(p,'gaussian',0,0.04);subplot(122),subimage(J2);title('加入高斯噪声后的图像，M=0,V=0.04');注：gaussian 高斯噪声 salt&petter 盐椒噪声 speckle 乘法噪声

3、Example：滤波1.Mean filterI = imread('coins.png');h = ones(5,5) / 25;%产生五行五列的1/25I2 = imfilter(I,h);imshow(I), title('Original Image');figure, imshow(I2);title('Filtered Image')；2.Median filterI=imread('G:a.tif') ;p=rgb2gray(I);J1=imnoise(p,'salt & pepper',0.

4、04);subplot(221),subimage(J1);title('加入盐椒噪声后的图像，密度D=0.04');J2=imnoise(p,'gaussian',0,0.04);subplot(222),subimage(J2);title('加入高斯噪声后的图像，M=0,V=0.04');J3=medfilt2(J1);subplot(223),subimage(J3);title('加入盐椒噪声中值滤波后的图像');J4=medfilt2(J2);subplot(224),subimage(J4);title('加

5、入高斯噪声中值滤波后的图像');Spatial Sharpening filtering空间锐化滤波Example：第一个I=imread('Water lilies.jpg');BW=rgb2gray(I);BW2=edge(BW,'sobel',0.02,'both'); figure,imshow(BW2)；第二个I = imread('bacteria.BMP');BW1 = edge(I,'prewitt',0.04)% 0.04为梯度阈值figure(1);imshow(I);figure(2)

6、;imshow(BW1);Basic operations of Digital Image processing ex: J = imcomplement(g); imshow(J),ex: I = imread('circuit.tif'); J = imresize(I,1.25);%图像放大 imshow(I) figure, imshow(J)ex: J = imresize(I,100 150, 'bilinear'); imshow(I) figure, imshow(J)ex: I = imread('pout.tif'); J

7、= imrotate(I,35);%35代表35度 imshow(J)ex： I=imread('cameraman.tif'); T = maketform('affine',cosd(45) -sind(45) 0; sind(45) cosd(45) 0; 0 0 1); tformfwd(10 20,T); I2 = imtransform(I,T); imshow(I2) Ex：I = imread('cameraman.tif');se = translate(strel(1), 25 25);J = imdilate(I,se)%图

8、像扩大;imshow(I), title('Original')figure, imshow(J), title('Translated');Translate an image Using affine transformation I=imread('cameraman.tif');T = maketform('affine',1 0 0; 1 2 0; 0 0 1);tformfwd(10 20,T);I2 = imtransform(I,T);imshow(I2)Frequency Domain Filtering H =

9、 HPFILTER(TYPE, M, N, D0, n) creates the transfer function of a highpass filter, H, of the specified TYPE and size (M-by-N). Valid values for TYPE, D0, and n are: 'ideal' Ideal highpass filter with cutoff frequency D0. n need not be supplied. D0 must be positive. 'btw' Butterworth hi

10、ghpass filter of order n, and cutoff D0. The default value for n is 1.0. D0 must be positive. 'gaussian' Gaussian highpass filter with cutoff (standard deviation) D0. n need not be supplied. D0 must be Positive.H = LPFILTER(TYPE, M, N, D0, n) creates the transfer function of a lowpass filter

11、, H, of the specified TYPE and size (M-by-N). To view the filter as an image or mesh plot, it should be centered using H = fftshift(H). Valid values for TYPE, D0, and n are: 'ideal' Ideal lowpass filter with cutoff frequency D0. n need not be supplied. D0 must be positive. 'btw' Butt

12、erworth lowpass filter of order n, and cutoff D0. The default value for n is 1.0. D0 must be positive. 'gaussian' Gaussian lowpass filter with cutoff (standard deviation) D0. n need not be supplied. D0 must be positive. Some Basic Gray Level TransformationsEx: I = imread('G:a.tif');

13、J = imadjust(I,0.3 0.7,);%增加图片的颜色感官imshow(I), figure, imshow(J)Ex:RGB1 = imread('G:a.tif');RGB2 = imadjust(RGB1,.2 .3 0; .6 .7 1,);imshow(RGB1), figure, imshow(RGB2)Histogram ProcessingEx:I=imread('G:a.tif') ;imhist (I); %灰度图像直方图Y,map=rgb2ind(I,256);imhist(Y,map); %索引图像直方图Ex:I=imread('G:a.tif') ;J=histeq( I) ;%增强图像对比image(J);Histogram equalizationI=imread('G:a.tif') ;J=histeq( I) ;image(J); Histogram specificationJ = HISTEQ(I,HGRAM)I=imread('G:a.tif') ;p=rgb2gray(I);image(p);subplot(221),subimage(p);title('原始图像');J1=imnoise(p,'gaussia