《调色盘操作实例》PPT课件.ppt

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1、Chapter 3Image Enhancementin the Spatial Domain(Part C),Image Processing,Department of Information Management,Leader University,139下书网提供大量行业资料，电子书，电脑教程下载,調色盤操作實例,假設有一3x3的圖形，使用的是8階的RGB色彩機制，且該圖形內容及調色盤如下：,修正後的結果,Histogram Equalization,Automatically determines a transformation function that seeks to pro

2、duce an output image that has a uniform histogram.When the uniform histogram is not the best approach,we may try the Histogram Matching or Histogram Specification.Histogram Matching allows us to specify the shape of the histogram.,Histogram Matching(Specification),The histogram equalization automati

3、cally determines a transformation function that seeks to produce an output image that has a uniform histogram.The method used to generate a processed image that has a specified histogram is called histogram matching or histogram specification.,Histogram Matching,Some Useful PDFs of pz(z),Specified P

4、DF Obtaining Procedures,Obtain the transformation function T(r)using Eq.(3.3-10).Use Eq.(3.3.11)to obtain the transformation function G(z).Obtain the inverse transformation function G-1.Obtain the output image by applying Eq.(3.3-12)to all the pixels in the input image.The result of this procedure w

5、ill be an image whose gray levels,z,have the specified probability density function pz(z),Discrete Version,Implementation Details,Each set of gray levels rj,sj,and zj,j=0,1,2,L-1,is one-dimensional array of dimension Lx1.All mappings from r to s and from s to z are simple table lookups between a giv

6、en pixel value and these arrays.Each of the elements of these arrays,for example,sk,contains two important pieces of information:The subscript k denotes the location of the element in the array,and s denotes the value at that location.We need to be concerned only with integer pixel values.,Histogram

7、 Matching,Procedures for Histogram Matching,Obtain the histogram of the given image.Use Eq.(3.3-13)to precompute a mapped level sk fro each level rk.Obtain the transformation function G from the given pz(z)using Eq.(3.3-14).Precompute zk for each value of sk using the iterative scheme defined in con

8、nection with Eq.(3.3-17).For each pixel in the original image,if the value of that pixel is rk,map this value to its corresponding level sk;then map level sk into the final level zk.Use the precomputed values from Steps(2)and(4)for these mappings.Step(5)implements two mappings.The first mapping is n

9、othing more than histogram equalization.,Original Image,Fail to Histogram Equalization,Since the problem with the transformationfunction in Fig.3.21(a)was caused by a large concentration of pixels in the originalimage with levels near 0.,Good Result by Histogram Matching Method,Manuallyspecifiedfunc

10、tion,G(z),G-1(z),Discussion of Histogram Matching,Histogram specification is a trial-and-error process.In general,there are no rules for specifying histograms,and one must resort to analysis on a case-by-case basis for any given enhancement task.,Local Enhancement,The histogram equalization and hist

11、ogram matching is the global processing,that is,the pixels are modified by a transformation function based on the gray-level content of an entire image.It is also necessary to enhance details over small areas in an image.The solution is to devise transformation functions based on the gray-level dist

12、ribution in the neighborhood of every pixel in the image.,Local Enhancement Example,Use of Histogram Statistics,The nth moment of r about its mean is defined as:,Mean and Variance,Meana measure of average gray level in an image.Variance or Standard deviationa measure o average contrast.,The Use of M

13、ean and Variance,The global mean and variance are measured over an entire image and are useful primarily for gross adjustments of overall intensity and contrast.The local mean and variance are used as the basis for making changes that depend on image characteristics in a predefined region about each

14、 pixel in the image.,Local Mean and Variance,Let(x,y)be the coordinates of a pixel in an image,and let Sxy denote a neighborhood(subimage)of specified size,centered at(x,y).,Original Image,Not obvious!,Mean and Variance Processing,Acceptable Result,Undesired effect!,Arithmetic/Logic Operationsbetwee

15、n Two Images,Arithmetic/logic operations involving images are performed on a pixel-by pixel basis between two or more images.Arithmetic operationsAddition,Subtraction,MultiplicationLogic operationsAND,OR,NOT,Enhancement Using Arithmetic/Logic Operations,Image Subtraction,The key usefulness of subtra

16、ction is the enhancement of difference between images.The results have to be limited into the gray level range 0.L-1.In tracking moving vehicles in a sequence of images,subtraction is used to remove all stationary components in an image.,Image Subtraction,Image Subtraction,Image Averaging,A noisy im

17、age g(x,y)can be defined as,Example for Image Averaging,Image Averaging,Basic of Spatial Filtering,Some neighborhood operations work with the values of the image pixels in the neighborhood and the corresponding values of a subimage that has the same dimensions as the neighbor.The subimage is called filter,mask,kernel,template,or window.The value in a filter subimage are referred to coefficients,Basics of Spatial Filtering,