數(shù)字圖像打印

1、(1) 名詞解釋RGB Red Green Blue ，紅綠藍三原色CMYK Cyan Magenta yellow blacK ,青、品紅、黃、黑，用于印刷的四分色HIS Horizontal Situation Indicator水平位置指示器FFT Fast Fourier Transform Algorithm (method)快速傅氏變換算法CWTcontinuous wavelet transform連續(xù)小波變換DCT Discrete Cosine Transform離散余弦變換DWTDiscreteWaveletTransform離散小波變換CCD Charge Coupled

2、 Device電荷耦合裝置Pixel:a digital image is composed of a finite number of elements,each of whichhas a particular lication and value,these elements are called pixel像素DCcomponent in frequency domain頻域直流分量GLHGray Level Histogram灰度直方圖Mather(basic)wavelet： a function (wave) used to generate a set of wavelets,

3、母小波，用于產(chǎn)生小波變換所需的一序列子小波Basis functions basis image:there is only one set ofk for any given f(x), then thek (x)are called basis functions 基函數(shù)基圖像Multi-scale analysis多尺度分析Gaussian function:is a function of the form:for some real constants a0, b,c 0,and e 2.718281828(Euler number)s.對 于 一 些 真 正 的 常 量 0,b,c

4、 0, 和e 2.718281828(歐拉數(shù) )。 高斯函數(shù)sharpening fil ter銳化濾波器Smoothing filter/convolution平滑濾波器 / 卷積smoothing filter are used for blurring and for noise reduction平滑濾波器用于模糊處理和降低噪聲 /卷積Image enhancement /image restoration圖像增強和圖像恢復(fù)空間域濾波 Spatial domain filtering：頻率域濾波Frequency domain filtering ： Frequencydomainfi

5、lteringwitha variablefrequency for the signal filtering以頻率作為變量對信號進行濾波空間分辨率： spatial resolution is a measure of thesmallest discernible detail in an image.圖像中可辨別的最小細節(jié)的度量灰度分辨率： Intensity resolution refers to the smallest discernible change in intensity level.灰度分辨率是指在灰度級中可分辨的最小變化取樣 sampling: Digitizing

6、 the coordinate values is called sampling.對坐標值進行數(shù)字化量化 quantization ： Digitizing the amplitude values is called quantization.對幅值數(shù)字化圖像壓縮： Image compression ,the art and science of reducing the amount of data requiredto represent an image. 圖像壓縮是一種減少描繪一幅圖像所需數(shù)據(jù)量的技術(shù)和科學.（2）問答題1. Cite one example of digital

7、 image processingAnswer: In the domain of medicalimage processingwe may need toinspecta certainclassof images generatedbyan electronmicroscopeto eliminatebright,isolated dots that are no interest.2.Cite one example of spatial operation舉一個空間操作的例子Answer:In the domain of medical image processing we may

8、 need to inspect a certainclass of images generated byan electron microscope to eliminatebright,isolated dotsthat are no interest.3.Cite one example of frequency domain operation from the following processingresult,make a generalcomment aboutidealhighpassfilter(figureB) and Gaussianhighpass filter(f

9、igure D)A. Original imageB. ideal highpass filterIn contrastto the ideallow pass filter,itisto letallthe signalsabovethe cutofffrequencyfc withoutloss,and to make allthe signalsbelow the cutofffrequency of FC without loss of.C. the result of ideal highpass filterD. Gaussian highpass filterHigh pass

10、filter,also known as "low resistancefilter",itis an inhibitoryspectrum of the low frequencysignaland retainhigh frequencysignalmodel (ordevice). High pass filter can make the high frequency components, while thehigh-frequency part of the frequency in the image of the sharp change in thegra

11、y area, which is often the edge of the object. So high pass filter can makethe image get sharpening processingE. The result of Gaussian filter3.The originalimage, the ideallowpass filterand Gaussian lowpass filterare shownbelow B nd C .D and E are the result of the either filter B or CA. Draw lines

12、to connect the filter with their resultB. Explain the difference of the two filtersDue to excessivecharacteristicsof the ideallow-passfiltertoo fastJun,it will produce a ringing phenomenon.Over characteristics of Gauss filter isvery flat, so it is not ringing4.What is the result when applying an ave

13、raging 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 called the samplingtheorem and the sampling theorem. The sampling theorem shows the relationshipbetween the samplingfrequencyand the s

14、ignalspectrum,and itis the basic basisof the continuoussignaldiscretization.In analog/ digitalsignalconversionprocess, when the sampling frequency fs.max greater than 2 times the highestfrequency present in the signal Fmax fs.max>2fmax, sampling digital signalcompletelyretainedtheinformationinthe

15、originalsignal,thegeneralpracticalapplicationassurancesamplingfrequencyis5 10 timeshigherthanthat of the signal of the high frequency; 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 filteringis to re

16、place the original image with the meanvalue of each pixel, but median filter replace the original image with the median value ofeach pixel.The value of mean and median is different.7.Fundamental Steps in images Digital image Processing數(shù)字圖像圖像處理的基本步驟image acquisition >image enhancement >image re

17、storation >Color image processing >w avelets >compression（壓縮） >morphological processing( 形態(tài)學理 ) >segmentation（分割） >representation and description （表示與描述） >recognition （識別）8.With the chromaticity diagram bellow give a brief description to the RGB color model.Andthese three colors

18、 enough to compose all visible colors?Answer ：Images represented in the RGB color model consist of three component images, onefor each primary color.These three colors enough to compose all visible colors（3）算法題1.The following matrix A is a 3*3 image and B is 3*3 Laplacian mask, what will be the resu

19、lting image? (Note that the elements beyond the border remain unchanged)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:x+y(

20、2)Compute the DFT of the image from (1)to get power spectrum F(u,v)of Fouriertransform.(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.Developan algorithmto generateap

21、proximationimage seriesshown in the followingfigure b* means of down sampling5.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 transform; （ 1）將輸

22、入圖像 f（ x， y）的（ -1） x+y 為中心的變換；(2) Compute the DFT of the image from (1) to get power spectrum F(u,v) of Fourier transform.計算圖像的DFT 從（ 1）得到的功率譜f（ u， v）的傅里葉變換。Multiply by a filter function h(u,v)乘以一個濾波器函數(shù)h(u,v).Compute the inverse DFT of the result in (3).計算（ 3）中的結(jié)果DFT 的逆Obtain the real part of the re

23、sult in (4).獲得（ 4）結(jié)果中的實部Multiply the result in (5) by（ -1）x+y（ -1） x+y 乘以（ 5）中的結(jié)果 .5.Lossless approaches Hoffman Coding無損方法- 霍夫曼編碼步驟：（ 1） create of source reductions by ordering the symbols under consideration and combining thelowest probability symbols into a single symbols that replaces them in th

24、e next source reduction.（ 2） Code each reduced source, starting with the smallest source and working back to the originalsource.（ 4）編程題1)There are two satellite photos of night as blew.Write a program with MATLAB to tell which is brighter代碼： A=imread （ 1.jgp） ;B=imread( 2.jpg);m,n=size(A);for i=1:mf

25、or j=1:nsum1=sum1+AI,j;endendavg1=sum1/m*n;r,c=size(B);for i=1:mfor j=1:nsum2=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

26、,j) ;note 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+1for j=1:col-n+1c=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);endendr=uint8(gray2);>> avg3=avgfilte

27、r(noise,3);>> avg5=avgfilter(noise,5);>> avg7=avgfilter(noise,7);>> subplot(221);imshow(noise);title(' 原噪聲圖 ');>> subplot(222);imshow(avg3);title('3*3 均值濾波圖 ');>> subplot(223);imshow(avg5);title('5*5 均值濾波圖 ');>> subplot(224);imshow(avg7);ti

28、tle('7*7 均值濾波圖 ');1 Design anadaptive local noise reduction filterand apply it to an image withGaussian noise . Compare the performanceof theadaptivelocalnoise reductionfilterwitharithmetic meanandgeometric meanfilter.Answer:clearclose all;數(shù)字圖像處理yy.bmp');gray=rgb2gray(rt);subplot(2,3,1);

29、imshow(rt);title('原圖像') ;subplot(2,3,2);imshow(gray);title('原灰度圖像 ') ;rtg=im2double(gray);rtg=imnoise(rtg,'gaussian',0,0.005)%加入均值為0，方差為 0.005 的高斯噪聲subplot(2,3,3);imshow(rtg);title('高噪點處理后的圖像');a,b=size(rtg);n=3;smax=7;nrt=zeros(a+(smax-1),b+(smax-1);for i=(smax-1)/2+

30、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('擴充后的圖像 ');nrt2=zeros(a,b);for i=n+1:a+nfor j=n+1:b+nfor m1=3:2m2=(m1-1)/2;c=nrt2(i-m2:i+m2,j-m2:j+m2);% 使用 7*7 的濾波器 Zmed=median(median(c);Zmin=min(min(c);Zmax=max(max(c)

31、;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);elsenrt2(i,j)=Zmed;endcontinue;endendendendnrt3=im2uint8(nrt2);figure;imshow(nrt3);title('自適應(yīng)中值濾波圖');2. ImplementWiener filterwith“ wiener2 ”function of

32、 MatLabto an image withGaussian noiseand compare the performance withadaptive local noise reductionfilter.代碼如下：數(shù)字圖像處理 yy.bmp');>>J=rgb2gray(I);>>K = imnoise(J,'gaussian',0,0.005);>>L=wiener2(K,5 5);>>subplot(1,2,1);imshow(K);title(' >>subplot(1,2,2);imsh

33、ow(L);title('高噪點處理后的圖像');維納濾波器處理后的圖像');3. Image smoothing with arithmetic averaging filter (spatial convolution).圖像平滑與算術(shù)平均濾波( 空間卷積 )。>> h=ones(3,3)/9;>> hh =0.11110.11110.11110.11110.11110.11110.11110.11110.1111>> x1=imfilter(x,h);>> subplot(121);imshow(x);title(&

34、#39;原圖 ');>> subplot(122);imshow(x1);title('經(jīng)過（ 3*3 ）鄰域平均后圖');>> h1=ones(5,5)/25;>> h1h1 =0.04000.04000.04000.04000.04000.04000.04000.04000.04000.04000.04000.04000.04000.04000.04000.04000.04000.04000.04000.04000.04000.04000.04000.04000.0400>> x2=imfilter(x,h1);>

35、;> subplot(121);imshow(x);title('原圖 ');>> subplot(122);imshow(x2);title('經(jīng)過（ 5*5 ）鄰域平均后圖 ');4.Make a comparisonof noise reduction by both median filterand averaging filter.進行比較和中值濾波的降噪平均濾波器。>> avgx=filter2(fspecial('average',5),x)/255;>> midx=medfilt2(x,5

36、,5);>> subplot(131);imshow(x);title('原圖 ');>> subplot(132);imshow(avgx);title('經(jīng)過（ 5*5 ）均值濾波圖');>> subplot(133);imshow(midx);title('經(jīng)過（ 5*5 ）中值濾波圖');5.Develop a program to implement a Gradient Mask to obtain edge of an object (in compare with the function pr

37、ovided by Matlab)開發(fā)一個程序來實現(xiàn)梯度面具來獲取一個對象的邊緣( 與Matlab提供的函數(shù))>> subplot(231);imshow(j);title('原圖 ');>> eSoble=edge(j,'sobel');>> subplot(232);imshow(eSoble);title('Soble圖 ');>> ePrewitt=edge(j,'prewitt');>> subplot(233);imshow(ePrewitt);title(&

38、#39;Prewitt圖');>> eRobert=edge(j,'roberts');>> subplot(234);imshow(eRobert);title('Robert圖 ');>> eLog=edge(j,'log');>> subplot(235);imshow(eLog);title('Log圖');>> eCanny=edge(j,'canny');>> subplot(236);imshow(eCanny);titl

39、e('Canny圖 ');6.Image enhancement with High-Boost Filtering Mask and compare with the result of the operation defined by equation圖像增強與High-Boost過濾面罩和與方程定義的操作的結(jié)果f ( x, y)2f ( x, y)g( x, y)2f ( x, y)f ( x, y)>> subplot(131);imshow(j);title('原圖 ');>> H=-1 -1 -1;-1 -9 -1;-1 -1

40、 -1;>> xhigh=filter2(H,j);>> subplot(132);imshow(xhigh,);title('高通濾波 ');>> 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);imshow(xm);title('掩膜處理 ');7 Count

41、 the number of pixels for each gray levels.計算像素的數(shù)量為每個灰色的水平。f j , j0,1, L1n j , j0,1, L1>> jpg=imread('F:19.jpg');>> grayjpg=rgb2gray(jpg);>> imshow(grayjpg);>> m,n=size(jpg);>> figure(1);>> imshow(jpg);>> gp=zeros(1,256);for i=1:256gp(i)=length(find(

42、jpg = (i-1);endfigure,bar(0:255,gp);8 Estimate probabilities of each gray levels.估計每個灰度級的概率。Pf ( f j )n j/ n, j0,1, L1m,n=size(jpg);figure(1);imshow(jpg);gp=zeros(1,256); %創(chuàng)建一個全零矩陣，1× 256，計算各灰度出現(xiàn)的概率for i=1:256gp(i)=length(find(jpg = (i-1)/(m*n);endfigure,bar(0:255,gp);9 Calculate cumulative dis

43、tribution function of each gray levels.計算每個灰度級的累積分布函數(shù)。C ( f )k0 Pf ( f j ), j0,1, k, L1jS1=zeros(1,256);tmp=0;for i=1:256tmp=tmp+gp(i);S1(i)=tmp;%各灰度的累計概率endfigure,plot(S1);10. Calculate gray levels of output image.計算輸出圖像的灰度值g = EQ (f)g i , i0,1, k, P1giINT(gmaxgmin)C( f )gmin0.5newGp=zeros(1,256);

44、%計算新的各灰度出現(xiàn)的概率S2=zeros(1,256);for i=1:256S2(i)=round(S1(i)*256); %將取整后的值存儲在endfor i=1:256S2newGp(i)=sum(gp(find(S2=i);endfigure,bar(0:255,newGp);11 Develop a program to decompose the two images into coefficients and then fuse the corresponding coefficients to obtain a fusion result.Observe the experi

45、ment result by trying different wavelets provided by Matlab and make 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('圖 1')subplot(222)imshow(x2)title('圖 2&

46、#39;)ca1,ch1,cv1,cd1=dwt2(x1,'haar');ca2,ch2,cv2,cd2=dwt2(x2,'haar');row,col=size(ca1);for i=1:rowfor j=1:colca(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);elsech(i,j)=ch2(i,j);cv(i,j)=

47、cv2(i,j);endif abs(cd1(i,j)>abs(cd2(i,j)cd(i,j)=cd1(i,j);elsecd(i,j)=cd2(i,j);endendendx=idwt2(ca,ch,cv,cd,'haar');imwrite(x,'a.png');subplot(223)imshow(x)title('融合之后的圖片')12.Develop a program with db2 wavelets decomposition to enhance the detail of the image.x1=imread('

48、;E:bwb.jpg ');x1=rgb2gray(x1);x2=imread('E:bwb.jpg ');x2=rgb2gray(x2);subplot(1,3,1)imshow(x1)title('圖像一 ')subplot(1,3,2)imshow(x2)title('圖像二 ')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(

49、1,k(2);temp=zeros(1,2);c(1:s1(1,1)=(c0(1:s1(1,1)+c1(1:s1(1,1)*0.5;p=waverec2(c,s0,wtype);p=uint8(p);subplot(1,3,3)imshow(p)title('融合之后的圖像')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,

50、6; 4,16,24,16,4; 1,4,6,4,1 W=plate/256;G_LOWER=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;EndSaveGDEC;End14. Using 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=121;242;121;c=c/16;fori=2:m-1forj=2:n-1b(i,j)=a(i-1,j-1)*c(1,1)+a(i-1,j)*c(1,2)+a(i-1,j+1)*c(1,3)+.a(i,j-1