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1.1 图像与图像处理的概念图像(Image)：使用各种观测系统以不同形式和手段观测客观世界而获得的，可以直接或间接作用于人眼并进而产生视觉的实体。
包括：·各类图片，如普通照片、X光片、遥感图片；·各类光学图像，如电影、电视画面；·客观世界在人们心目中的有形想象以及外部描述，如绘画、绘图等。
数字图像：为了能用计算机对图像进行加工，需要把连续图像在坐标空间和性质空间都离散化，这种离散化了的图像是数字图像。
图像中每个基本单元叫做图像的元素，简称像素（Pixel）。
数字图像处理（Digital Image Processing）：是指应用计算机来合成、变换已有的数字图像，从而产生一种新的效果，并把加工处理后的图像重新输出，这个过程称为数字图像处理。
也称之为计算机图像处理（Computer Image Processing）。
1.2 图像处理科学的意义1.图像是人们从客观世界获取信息的重要来源·人类是通过感觉器官从客观世界获取信息的，即通过耳、目、口、鼻、手通过听、看、味、嗅和接触的方式获取信息。
在这些信息中，视觉信息占70％。
·视觉信息的特点是信息量大，传播速度快，作用距离远，有心理和生理作用，加上大脑的思维和联想，具有很强的判断能力。
·人的视觉十分完善，人眼灵敏度高，鉴别能力强，不仅可以辨别景物，还能辨别人的情绪。
2.图像信息处理是人类视觉延续的重要手段非可见光成像。
如：γ射线、X射线、紫外线、红外线、微波。
利用图像处理技术把这些不可见射线所成图像加以处理并转换成可见图像，可对非人类习惯的那些图像源进行加工。
3.图像处理技术对国计民生有重大意义图像处理技术发展到今天，许多技术已日益趋于成熟，应用也越来越广泛。
它渗透到许多领域，如遥感、生物医学、通信、工业、航空航天、军事、安全保卫等。
1.3 数字图像处理的特点1. 图像信息量大每个像素的灰度级至少要用6bit（单色图像）来表示，一般采用8bit（彩色图像），高精度的可用12bit 或16bit。
光电图像处理复习复习资料：课件，课堂笔记，参考书1、2考题类型：简答题，问答题，计算题考试分值：70％（平时30％）答疑时间：根据考试时间确定主要内容一、数字图像处理的基础1、图像的定义A、二维或三维景物呈现在人心目中的影像。
B、自然界的物体经可见光的照射由人的视觉系统所感知的景物。
C、任何数据场在空间的有序排列。
D、图像是对客观存在事物的一种相似性的生动模仿与描述，使物体的一种不完全的、不精确的描述，但是某种意义上是适当的表示。
2、图像分类物理图像：是指物质或能量的实际分布。
虚拟图像：采用数学方法，将由概念形成的物体（不是实物）进行表示的图像，即采用数学建模的方式，利用成像几何原理，在计算机上制作的。
模拟图像：可用连续函数来描述，光照位置和强度均为连续变化。
数字图像，可用矩阵或数组描述，光照位置和强度均为离散化的。
（这有个公式。
）3、数字图像处理（概念）是指应用计算机来来合成、变换已有的数字图像，从而产生一种新的效果，并把加工处理的图像重新输出的过程。
三个层次A. 图像处理：对图像进行各种加工，以改善图像的视觉效果；强调图像之间进行的变换。
（图像到图像的过程）B. 图像分析：对图像中感兴趣的目标进行提取和分割，获得目标的客观信息（特点或性质），建立对图像的描述，以观察者为中心研究客观世界。
（图像到数据的过程）C. 图像理解：研究图像中各目标的性质和他们之间的相互关联；得出对图像内容含义的理解及原来客观场景的解释；以客观世界为中心，借助知识、经验来推理、认识客观世界，属于高层操作。
（图像到抽象的过程）4、数字图像处理的内容图像获取和表示：该过程主要是把模拟信号转化为计算机所能接受的数字形式，以及把数字图像显示和表现出来。
这一过程主要包括获取图像、光电转换及数字化等几个步骤。
图像增强：改善图像主观视觉感受质量。
没有最好的方法，只能选择比较合适的方法。
图像复原：当造成图像退化原因已知，可以对图像进行复原。
(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)。
