数字图像处理第三版中文的答案解析冈萨雷斯

直方图均衡化实例
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直方图均衡化实例
直方图均衡化实例
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直方图均衡化实例
3.3.2 直方图规定化
• 直方图匹配（规定化） histogram matching (specification): 使图像具有规定的直方图分布。 基本思想：原始直方图为 想 原 直方图为pr(r)，指定直方图 • 基本 为pz(z)，寻找一灰度级变换函数z = F(r)， 实现 pr(r) →pz(z)
开始叠加。
3.3.2 直方图规定化
• 直方图规定化的完整步骤：
1. 计算原图像直方图pr(r) 2. 利用 获得下表： r s r0 s0 r1 s1
r
3.3.2 直方图规定化
3. 根据规定的pz(z)，进一步利用
ˆ sk 0 G z
获得下表： r s z r0 s0 z0 r1 s1 z1 … … … rL-1 sL-1 zL-1
3.3.1 直方图均衡化
• 在MATLAB中，函数histeq提供此功能，其一般形式为： g = histeq(f, nlev) 其中f为输入图像，nlev为输出灰度级，
>> >> >> >> >> >> >> >> f = imread('pollen.tif'); imshow(f) fi figure, i imhist(f) hi t(f) ylim('auto') g = histeq(f, 256); figure, imshow(g) figure, imhist(g) ylim('auto')

饱和度指 的是一个 最大值。 超过这个 值的灰度 级将被剪 切掉。
N和k取不同值时存储所需的比特数
空间和灰度分辨率
（1）空间分辨率：图像空间中可分辨的最小细节。一般 用单位长度上采样的像素数目或单位长度上的线对数目表 示。例如，举一个质量概念，报纸用75dpi的分辨率来印刷， 杂志是133dpi，光鲜的小册子是175dpi，您正在看的书是 以2044dpi印刷的。空间分辨率的度量必须针对空间单位来 规定才有意义。 （2）灰度分辨率：图像灰度级中可分辨的最小变化。一 般用灰度级或比特数表示。灰度级数通常是2的整数次幂。 最通用的是8比特。通常说一副被量化为256级的图像有8比 特的灰度分辨率。
的动 地 量 胶 一一 从 ， 片 行次 左 且 每 。， 到 传 旋 输右感转 出线器一 图性完个 像移整增
2.3.2 使用条带传感器获取图像
1.使用一个线性传 感器带获取图像 2.使用一个环形传 感器带获取图像
• 比单个传感器更常用的几何结构是由内嵌传感器 形式组成的传感器带。感知设备内嵌有4000个或 更多的传感器。内嵌传感器常用于航空成像应用 中，飞行器以恒定的高度和速度飞过被成像的地 区。成像传感器带一次给出一幅图像的一行，传 感器带的运动完成二维图像的另一个维度。 • 以圆环形方式安装的传感器带用于医学和工业成 像，以得到三维物体的剖面(切片)图像，传感器 的输出必须由重建算法处理，目的是把感知数据 转换为有意义的剖面图像。
2.3.4 简单的图像形成模型（数学模型）
f ( x, y ) i ( x , y ) r ( x , y ) 其中： f ( x, y )是图像幅度分布 i ( x, y )是入射场分布，取决于照射源特性 r ( x, y )是反射系数分布, 取决于成像物体的特性

y x1.2 数字图像成像技术的起源和发展1．数字图像处理的起源2．数字图像处理的发展1)依赖于计算机软硬件技术;2)从新闻媒介、空间探索到医学图像地球遥感监测天1.2 数字图像成像技术的起源和发展学图像、地球遥感监测、天文学等领域。

3)伦琴发现X 射线于1901获得诺贝尔物理奖;Godfrey N. Hounsfield 和Allan M. Cormack 发明CT 技术于1979年获得诺贝尔医学奖.1.3 数字图像处理实例•电磁波成像：151.3数字图像处理实例•伽马射线成像：16正电子放射断层成像（PET）1.3数字图像处理实例•X射线成像：17X射线胸透成像1.3数字图像处理实例•X射线成像：18 X射线血管照相术计算机轴向断层成像（CAT）1.3数字图像处理实例•紫外光成像：19荧光显微镜细胞结构成像效果1.3数字图像处理实例•可见光及红外光成像：应用最为丰富20微处理器显微照片音频CD 表面显微照片1.3数字图像处理实例•可见光及红外光成像：21NASA 的LANDSAT 卫星主要波段1.3数字图像处理实例•可见光及红外光成像：221.3数字图像处理实例•可见光及红外光成像：23安德鲁飓风的多光谱图像（由NOAA 提供）1.3数字图像处理实例•可见光及红外光成像：24世界之夜（热红外成像）1.3数字图像处理实例•可见光及红外光成像：25工业检测1.3数字图像处理实例•可见光及红外光成像：26自动识别1.3数字图像处理实例•微波成像：27航天雷达成像1.3数字图像处理实例•无线电波成像：28磁共振成像（MRI ）1.3数字图像处理实例•其他成像方式：声波、超声波、电子、计算机合成291.4 数字图像处理基本步骤301.5 通用图像处理系统组成31。

(1) 名词解释RGB Red Green Blue，红绿蓝三原色CMYK Cyan Magenta yellow blacK , 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) 快速傅氏变换算法CWT continuous wavelet transform 连续小波变换DCT Discrete Cosine Transform 离散余弦变换DWT DiscreteWaveletTransform 离散小波变换CCD Charge Coupled 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 set 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 基函数基图像Multi-scale analysis 多尺度分析Gaussian function 高斯函数sharpening filter 锐化滤波器Smoothing filter/convolution 平滑滤波器/卷积Image enhancement /image restoration 图像增强和图像恢复（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 frequency domain operation from the following processing result, make a general comment about ideal highpass filter (figure B) and Gaussian highpass filter(figure D)A. Original imageB. ideal highpass filterIn 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 frequency of FC without loss of.C. the result of ideal highpass filterD. 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 high 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 filter and Gaussian lowpass filter are shown below B nd C .D and E are the result of the eitherfilter B or CA. Draw lines to connect the filter with their resultB. Explain the difference of the two filtersDue to excessive characteristics of the ideal low-pass filter too fast Jun, it will produce a ringing 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?5.State the concept of the Nyquist sampling theorem from the figure belovyThe law of sampling process should be followed, also called the sampling theorem and the sampling theorem. The sampling theorem showsthe 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 greater 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; sampling theorem, also known as the Nyquist theorem6.A mean filter is a linear filter but a median filter is not, why?Mean filter is a typical linear filtering algorithm, it is to point to in the target pixels in the image to a template, this template including its surrounding adjacent pixels and the pixels in itself.To use in the template to replace all the pixels of average pixelvalues.Linear filter, median filter, also known as the main method used in the bounded domain average method.Median filter is a kind of commonly used nonlinear smoothing filter and its basic principle is to put the little value in a digital image or sequence to use value at various points in the field of a point at which the value to replace, its main function is to let the surrounding pixel gray value differences between larger pixel change with the surrounding pixels value close to the values, which can eliminate the noise of the isolated points, so median filter to filter out the salt and pepper noise image is very effective.（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 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:(1) Multiply the input image f(x,y) by （-1）x+y tocenter the transform;(2) Compute the DFT of the image from (1) to get power spectrumF(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 generate approximation image series shown in the following figure b** means of down sampling.（4）编程题There are two satellite photos of night as blew.Write a programwith MATLAB to tell which is brighterAn 8*8 image f(i,i) has gray levels given by the following equation:f(i,i)=|i-j|, i,j=0,1 (7)Write a program to find the output image obtained by applying a 3*3 median filter on the image f(i,j) ;note that the border pixels remain unchanged.Answer:1．Design 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=rgb2gray(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-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+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的滤波器Zmed=median(median(c));Zmin=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);elsenrt2(i,j)=Zmed;endcontinue;endendendendnrt3=im2uint8(nrt2);figure;imshow(nrt3);title('自适应中值滤波图');2. Implement 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]);>>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)。