数字图像打印

1、(1) 名词解释RGB Red Green Blue，红绿蓝三原色CMYK Cyan Magenta yellow blacK , 青、品红、黄、黑，用于印刷的四分色HISHorizontal Situation Indicator 水平位置指示器FFTFast Fourier Transform Algorithm (method) 快速傅氏变换算法CWTcontinuous wavelet transform 连续小波变换DCTDiscrete Cosine Transform 离散余弦变换DWT DiscreteWaveletTransform离散小波变换CCDCharge Couple

2、d Device 电荷耦合装置Pixel: a digital image is composed of a finite number of elements,each of which has a particular lication and value,these elements are called pixel 像素DC component in frequency domain 频域直流分量GLH  Gray Level Histogram 灰度直方图Mather(basic)wavelet：a function (wave) used to generate a se

3、t of wavelets, 母小波，用于产生小波变换所需的一序列子小波Basis functions basis image: there is only one set of k for any given f(x), then the k (x) are called basis functions 基函数基图像Multi-scale analysis 多尺度分析Gaussian function:is a function of the form: for some real constants a 0, b, c 0, and e 2.718281828 (Eulers number

4、).对于一些真正的常量0,b,c 0,和e2.718281828(欧拉数)。 高斯函数sharpening filter 锐化滤波器Smoothing filter/convolution 平滑滤波器/卷积smoothing filter are used for blurring and for noise reduction平滑滤波器用于模糊处理和降低噪声/卷积Image enhancement /image restoration图像增强和图像恢复空间域滤波Spatial domain filter

5、ing：频率域滤波Frequency domain filtering：Frequency domain filtering with a variable frequency for the signal filtering以频率作为变量对信号进行滤波空间分辨率：spatial resolution is a measure of the smallest discernible detail in an image.图像中可辨别的最小细节的度量灰度分辨率：Intensity resolution refers to the smallest discernible change in in

6、tensity level.灰度分辨率是指在灰度级中可分辨的最小变化取样sampling: Digitizing the coordinate values is called sampling.对坐标值进行数字化量化 quantization：Digitizing the amplitude values is called quantization.对幅值数字化图像压缩：Image compression ,the art and science of reducing the amount of data required to represent an image.图像压缩是一种减少描

7、绘一幅图像所需数据量的技术和科学.（2）问答题1. Cite one example of digital image processingAnswer: In the domain of medical image processing we may need to inspect a certain class of images generated by an electron microscope to eliminate bright, isolated dots that are no interest. 2.Cite one example of spatial operatio

8、n举一个空间操作的例子Answer: In the domain of medical image processing we may need to inspect a certain class of images generated by an electron microscope to eliminate bright, isolated dots that are no interest. 3.Cite one example of frequency domain operation from the following processing result, make a gen

9、eral comment about ideal highpass filter (figure B) and Gaussian highpass filter(figure D) A. Original image B. ideal highpass filter In contrast to the ideal low pass filter, it is to let all the signals above the cutoff frequency fc without loss, and to make all the signals below the cutoff freque

10、ncy of FC without loss of.C. the result of ideal highpass filter D. Gaussian highpass filterHigh pass filter, also known as "low resistance filter", it is an inhibitory spectrum of the low frequency signal and retain high frequency signal model (or device). High pass filter can make the hi

11、gh frequency components, while the high-frequency part of the frequency in the image of the sharp change in the gray area, which is often the edge of the object. So high pass filter can make the image get sharpening processingE. The result of Gaussian filter3.The original image, the ideal lowpass fi

12、lter and Gaussian lowpass filter are shown below B nd C .D and E are the result of the either filter B or CA. Draw lines to connect the filter with their resultB. Explain the difference of the two filters Due to excessive characteristics of the ideal low-pass filter too fast Jun, it will produce a r

13、inging phenomenon.Over characteristics of Gauss filter is very flat, so it is not ringing4.What is the result when applying an averaging mask with the size 1X1?No change5.State the concept of the Nyquist sampling theorem from the figure belovyThe law of sampling process should be followed, also call

14、ed the sampling theorem and the sampling theorem. The sampling theorem shows the relationship between the sampling frequency and the signal spectrum, and it is the basic basis of the continuous signal discretization. In analog / digital signal conversion process, when the sampling frequency fs.max g

15、reater than 2 times the highest frequency present in the signal Fmax fs.max>2fmax, sampling digital signal completely retained the information in the original signal, the general practical application assurance sampling frequency is 5 10 times higher than that of the signal of the high frequency;

16、 sampling theorem, also known as the Nyquist theorem6.A mean filter is a linear filter but a median filter is not, why?The basic principle of linear filtering is to replace the original image with the mean value of each pixel, but median filter replace the original image with the median value of eac

17、h pixel.The value of mean and median is different.7.Fundamental Steps in images Digital image Processing 数字图像图像处理的基本步骤image acquisition>image enhancement>image restoration>Color image processing>wavelets>compression（压缩）>mo

18、rphological processing(形态学理)>segmentation（分割）>representation and description（表示与描述）>recognition（识别）8.With the chromaticity diagram bellow give a brief description to the RGB color model. And these three colors enough to compose all visible colors?Answer：Images represented in

19、the RGB color model consist of three component images, one for each primary color.These three colors enough to compose all visible colors（3）算法题1.The following matrix A is a 3*3 image and B is 3*3 Laplacian mask, what will be the resulting image? (Note that the elements beyond the border remain uncha

20、nged)2.Develop an algorithm to obtain the processing result B from original image A3.Develop an algorithm which computes the pseudocolor image processing by means of fourier tramsformAnswer：The steps of the process are as follow:(1) Multiply the input image f(x,y) by （-1）x+y to center the transform;

21、 (2) Compute the DFT of the image from (1) to get power spectrum F(u,v) of Fourier transform.(3) Multiply by a filter function h(u,v) .(4) Compute the inverse DFT of the result in (3).(5) Obtain the real part of the result in (4).(6) Multiply the result in (5) by（-1）x+y4.Develop an algorithm to gene

22、rate approximation image series shown in the following figure b* means of down sampling 5.Develop an algorithm which implements frequency domain filtering by means of Fourier transform. Answer：The steps of the process are as follow:(1) Multiply the input image f(x,y) by （-1）x+y to center the transfo

23、rm; （1）将输入图像f（x，y）的（-1）x+y为中心的变换；(2) Compute the DFT of the image from (1) to get power spectrum F(u,v) of Fourier transform.计算图像的DFT从（1）得到的功率谱f（u，v）的傅里叶变换。Multiply by a filter function h(u,v) 乘以一个滤波器函数h(u,v) .Compute the inverse DFT of the result in (3).计算（3）中的结果DFT的逆Obtain the real part of the res

24、ult in (4).获得（4）结果中的实部Multiply the result in (5) by（-1）x+y （-1）x+y 乘以（5）中的结果.5.Lossless approachesHoffman Coding 无损方法 - 霍夫曼编码步骤：（1） create of source reductions by ordering the symbols under consideration and combining the lowest probability symbols into a single symbols that replaces them in the nex

25、t source reduction.（2） Code each reduced source, starting with the smallest source and working back to the original source.（4）编程题1)There are two satellite photos of night as blew.Write a program with MATLAB to tell which is brighter 代码：A=imread（1.jgp）; B=imread(2.jpg);m,n=size(A);for i=1:mfor j=1:n

26、sum1=sum1+AI,j;endendavg1=sum1/m*n;r,c=size(B);for i=1:mfor j=1:n sum2=sum2+BI,j;endendavg2=sum2/m*n;2)An 8*8 image f(i,i) has gray levels given by the following equation:f(i,i)=|i-j|, i,j=0,1.,7Write a program to find the output image obtained by applying a 3*3 median filter on the image f(i,j) ;no

27、te that the border pixels remain unchanged.Ansewr:function r=avgfilter(gray,n) a(1:n,1:n)=1;row,col=size(gray);gray1=double(gray); gray2=gray1; for i=1:row-n+1 for j=1:col-n+1 c=gray1(i:i+(n-1),j:j+(n-1).*a; s=sum(sum(c); gray2(i+(n-1)/2,j+(n-1)/2)=s/(n*n); end end r=uint8(gray2);>> avg3=avgfi

28、lter(noise,3);>> avg5=avgfilter(noise,5);>> avg7=avgfilter(noise,7);>> subplot(221);imshow(noise);title('原噪声图');>> subplot(222);imshow(avg3);title('3*3均值滤波图');>> subplot(223);imshow(avg5);title('5*5均值滤波图');>> subplot(224);imshow(avg7);title

29、('7*7均值滤波图');1Design an adaptive local noise reduction filter and apply it to an image with Gaussian noise. Compare the performance of the adaptive local noise reduction filter with arithmetic mean and geometric mean filter. Answer:clearclose all;rt=imread('E:数字图像处理yy.bmp');gray=rgb2

30、gray(rt);subplot(2,3,1);imshow(rt);title('原图像') ;subplot(2,3,2);imshow(gray);title('原灰度图像') ;rtg=im2double(gray);rtg=imnoise(rtg,'gaussian',0,0.005)%加入均值为0，方差为0.005的高斯噪声subplot(2,3,3);imshow(rtg);title('高噪点处理后的图像');a,b=size(rtg);n=3;smax=7;nrt=zeros(a+(smax-1),b+(smax

31、-1);for i=(smax-1)/2+1):(a+(smax-1)/2) for j=(smax-1)/2+1):(b+(smax-1)/2) nrt(i,j)=rtg(i-(smax-1)/2,j-(smax-1)/2); endendfigure;imshow(nrt);title('扩充后的图像');nrt2=zeros(a,b); for i=n+1:a+n for j=n+1:b+n for m1=3:2 m2=(m1-1)/2; c=nrt2(i-m2:i+m2,j-m2:j+m2);%使用7*7的滤波器 Zmed=median(median(c); Zmin=

32、min(min(c); Zmax=max(max(c); A1=Zmed-Zmin; A2=Zmed-Zmax; if(A1>0&&A2<0) B1=nrt2(i,j)-Zmin; B2=nrt2(i,j)-Zmax; if(B1>0&&B2<0) nrt2(i,j)= nrt2(i,j); else nrt2(i,j)=Zmed; end continue; end end endendnrt3=im2uint8(nrt2);figure;imshow(nrt3);title('自适应中值滤波图');2. Impleme

33、nt Wiener filter with “wiener2” function of MatLab to an image with Gaussian noise and compare the performance with adaptive local noise reduction filter.代码如下：>> I=imread('E:数字图像处理yy.bmp');>>J=rgb2gray(I);>>K = imnoise(J,'gaussian',0,0.005);>>L=wiener2(K,5 5);

34、>>subplot(1,2,1);imshow(K);title('高噪点处理后的图像');>>subplot(1,2,2);imshow(L);title('维纳滤波器处理后的图像');3. Image smoothing with arithmetic averaging filter (spatial convolution). 图像平滑与算术平均滤波(空间卷积)。>> h=ones(3,3)/9;>> hh = 0.1111 0.1111 0.1111 0.1111 0.1111 0.1111 0.1111

35、 0.1111 0.1111>> x1=imfilter(x,h);>> subplot(121);imshow(x);title('原图');>> subplot(122);imshow(x1);title('经过（3*3）邻域平均后图');>> h1=ones(5,5)/25;>> h1h1 = 0.0400 0.0400 0.0400 0.0400 0.0400 0.0400 0.0400 0.0400 0.0400 0.0400 0.0400 0.0400 0.0400 0.0400 0.040

36、0 0.0400 0.0400 0.0400 0.0400 0.04000.0400 0.0400 0.0400 0.0400 0.0400>> x2=imfilter(x,h1);>> subplot(121);imshow(x);title('原图');>> subplot(122);imshow(x2);title('经过（5*5）邻域平均后图');4.Make a comparison of noise reduction by both median filter and averaging filter. 进行比较

37、和中值滤波的降噪平均滤波器。 >> avgx=filter2(fspecial('average',5),x)/255;>> midx=medfilt2(x,5,5);>> subplot(131);imshow(x);title('原图');>> subplot(132);imshow(avgx);title('经过（5*5）均值滤波图');>> subplot(133);imshow(midx);title('经过（5*5）中值滤波图');5.Develop a pr

38、ogram to implement a Gradient Mask to obtain edge of an object (in compare with the function provided by Matlab)开发一个程序来实现梯度面具来获取一个对象的边缘(与Matlab提供的函数)>> subplot(231);imshow(j);title('原图');>> eSoble=edge(j,'sobel');>> subplot(232);imshow(eSoble);title('Soble图'

39、);>> ePrewitt=edge(j,'prewitt');>> subplot(233);imshow(ePrewitt);title('Prewitt图');>> eRobert=edge(j,'roberts');>> subplot(234);imshow(eRobert);title('Robert图');>> eLog=edge(j,'log');>> subplot(235);imshow(eLog);title('L

40、og图');>> eCanny=edge(j,'canny');>> subplot(236);imshow(eCanny);title('Canny图');6.Image enhancement with High-Boost Filtering Mask and compare with the result of the operation defined by equation 图像增强与High-Boost过滤面罩和与方程定义的操作的结果>> subplot(131);imshow(j);title('

41、;原图');>> H=-1 -1 -1;-1 -9 -1;-1 -1 -1;>> xhigh=filter2(H,j);>> subplot(132);imshow(xhigh,);title('高通滤波');>> jdouble=double(j);>> M=1 1 1;1 1 1;1 1 1/9;>> xmask=double(xhigh);>> xmask2=filter2(M,xmask);>> xm=xmask-xmask2;>> subplot(133

42、);imshow(xm);title('掩膜处理');7Count the number of pixels for each gray levels. 计算像素的数量为每个灰色的水平。>> jpg=imread('F:19.jpg');>> grayjpg=rgb2gray(jpg);>> imshow(grayjpg);>> m,n=size(jpg);>> figure(1);>> imshow(jpg);>> gp=zeros(1,256); for i=1:256 gp

43、(i)=length(find(jpg = (i-1);end figure,bar(0:255,gp);8Estimate probabilities of each gray levels. 估计每个灰度级的概率。 m,n=size(jpg); figure(1); imshow(jpg); gp=zeros(1,256); %创建一个全零矩阵，1×256，计算各灰度出现的概率 for i=1:256 gp(i)=length(find(jpg = (i-1)/(m*n); end figure,bar(0:255,gp); 9Calculate cumulative distr

44、ibution function of each gray levels. 计算每个灰度级的累积分布函数。 S1=zeros(1,256); tmp=0; for i=1:256 tmp=tmp+gp(i); S1(i)=tmp; %各灰度的累计概率 end figure,plot(S1); 10. Calculate gray levels of output image. 计算输出图像的灰度值g = EQ (f)newGp=zeros(1,256); %计算新的各灰度出现的概率 S2=zeros(1,256); for i=1:256S2(i)=round(S1(i)*256); %将取整

45、后的值存储在S2 endfor i=1:256newGp(i)=sum(gp(find(S2=i); end figure,bar(0:255,newGp);11Develop a program to decompose the two images into coefficients and then fuse the corresponding coefficients to obtain a fusion result. Observe the experiment result by trying different wavelets provided by Matlab and m

46、ake necessary comparisons.x1=imread('E:bwb.jpg');x1=rgb2gray(x1);x1=double(x1)/255;x2=imread('E:bwb.jpg');x2=rgb2gray(x2);x2=double(x2)/255; subplot(221)imshow(x1) title('图1')subplot(222)imshow(x2) title(' 图2')ca1,ch1,cv1,cd1=dwt2(x1,'haar');ca2,ch2,cv2,cd2=dw

47、t2(x2,'haar');row,col=size(ca1);for i=1:row for j=1:col ca(i,j)=(ca1(i,j)+ca2(i,j)/2; if abs(ch1(i,j)>abs(ch2(i,j) ch(i,j)=ch1(i,j); elseif abs(ch1(i,j)<abs(ch2(i,j) ch(i,j)=ch2(i,j); cv(i,j)=cv1(i,j); else ch(i,j)=ch2(i,j); cv(i,j)=cv2(i,j);endif abs(cd1(i,j)>abs(cd2(i,j) cd(i,j)=c

48、d1(i,j);else cd(i,j)=cd2(i,j); endendendx=idwt2(ca,ch,cv,cd,'haar');imwrite(x,'a.png');subplot(223)imshow(x)title('融合之后的图片 ')12.Develop a program with db2 wavelets decomposition to enhance the detail of the image.x1=imread('E:bwb.jpg');x1=rgb2gray(x1);x2=imread('E

49、:bwb.jpg');x2=rgb2gray(x2);subplot(1,3,1)imshow(x1)title('图像一')subplot(1,3,2)imshow(x2)title('图像二')x1=double(x1);x2=double(x2);zt=3;wtype='haar'c0,s0=wavedec2(x1,zt,wtype);c1,s1=wavedec2(x2,zt,wtype);k=size(c1);c=zeros(1,k(2);temp=zeros(1,2);c(1:s1(1,1)=(c0(1:s1(1,1)+c1(1

50、:s1(1,1)*0.5;p=waverec2(c,s0,wtype);p=uint8(p);subplot(1,3,3)imshow(p)title('融合之后的图像')13. The gaussian pyramid decomposition of the imageLevel=5；Img=imread(lena.gif);G0=double(img);row,col=size(G0);Plate1,4,6,4,1;4,16,24,16,4;6,24,36,24,6; 4,16,24,16,4; 1,4,6,4,1W=plate/256;G_LOWER

51、=G0;GDEC=zeros(row,col,level);GDEC=GDEC-1;  For(flag=1:level)  G_LOWER=reduce2(G_LOWER);  DECIM=conv2(G_LOWER,W,same);decrow,deccol=size(DECIM);  Figure,imshow(uint8(DECIM);title(level,num2str(flag);GDEC(1:decrow,1:deccol,flag)=DECIM;EndSave GDEC;End14. U

52、sing the weighted average of the mask to realize digital image smoothing;a=imread('f:123.jpg');a=rgb2gray(a);imshow(a);a=double(a);m,n=size(a);b=zeros(m,n);c=1 2 1; 2 4 2;1 2 1;c=c/16;for i=2:m-1    for j=2:n-1b(i,j)=a(i-1,j-1)

53、*c(1,1)+a(i-1,j)*c(1,2)+a(i-1,j+1)*c(1,3)+.       a(i,j-1)*c(2,1)+a(i,j)*c(2,2)+a(i,j+1)*c(2,3)+.           a(i+1,j-1)*c(3,1)+a(i+1,j)*c(3,2)+a(i+1,j+1)*c(3,3);   endendb=mat2gray(b);figure,imshow(b);15. Digital image by using Laplacian sharpeningh=-1 -1 -1;-1 9 -1;-1 -1 -1;a=imread('d:jing.jpg');a=rgb2gray(a);imshow(a);a=double(a);r,c=size(a);b=zeros(r,c);