Topic-12_Color_Image_Processing 彩色圖像處理

1、lColor Image ProcessingnTechniques in Pseudo-Color Image processinguIntensity SlicinguColor LUT DesignsnNoise in Color ImagesuNoise in RGBuNoise in HSVnSmoothing of Color ImagesnSharpening of Color ImagesEECE-5626: Color Image Processing1/24lA black-and-white image is transformed into a color image

2、using pixel-point processing (color LUTs).lThe main application is for human visualization and interpretation of gray-level details.lThe principal challenge is to select a path through the color space that can “amplify” the low contrast details in the image.lTechniques:nIntensity SlicingnColor LUT d

3、esign (Gray-level to Color Transformations)EECE-5626: Color Image Processing2/24lIf an image is viewed as a 2-D intensity function, then this intensity can be sliced by a plane parallel to the coordinate plane. lIf we assign different colors on each side of the plane, any pixel with gray level above

4、 the plane is coded with one color and any pixel below the plane is coded with another color. lThe result is a two color image as shown.lThis approach can be extended to more than two planes.EECE-5626: Color Image Processing3/24lExampleTwo-level slicing:In this example, the gray-level 255 is assigne

5、d a color yello while the rest of the gray-levels, 0 254, are assigned color blue. The gray-level 255 signifies failure in a weld.EECE-5626: Color Image Processing4/24lExample: Eight-level slicingEECE-5626: Color Image Processing5/24lThese Look-Up Tables define a mapping from the 1-D gray-level spac

6、e to the 3-D RGB color space. This mapping is given by three PVMs, one for each primary color. Almost all modern image processing boards contain (programmable) hardware tables between a frame buffer and a monitor.EECE-5626: Color Image Processing6/24lThese gray-level transformations essentially are

7、unique paths from the black intensity (0) to the white intensity (1) in such a way that the assigned colors can aid in visualizing and identifying image features.EECE-5626: Color Image Processing7/24lDesign of these transformations requires a good knowledge of the color theory. Specifically, if we a

8、ssign complementary colors to the adjacent gray-levels then we can visualize these gray-levels better.EECE-5626: Color Image Processing8/24lSome typical transformations are:nSpectrum or RainbowEECE-5626: Color Image Processing9/24lTypical Transformations (continued)nSpectrum or Rainbow: ExampleEECE-

9、5626: Color Image Processing10/24lTypical Transformations (continued)nSoft Colors: Here each transformation is sine function of the same frequency but different phase. This produces soft colors and is useful in enhancing busy details.EECE-5626: Color Image Processing11/24lTypical Transformations (co

10、ntinued)nSoft Colors: ExampleEECE-5626: Color Image Processing12/24lTypical Transformations (continued)nSoft Colors: ExampleEECE-5626: Color Image Processing13/24lTypical Transformations (continued)nBitcolor: Here each bit plane is assigned a different color. For example, if we represent a pixel in

11、binary asb7b6b5b4b3b2b1b0then one possible scheme is:R R R G G G B BEECE-5626: Color Image Processing14/24lTypical Transformations (continued)nRandom : Here color schemes are assigned in a random fashion. Useful for images that have very smooth appearance.EECE-5626: Color Image Processing15/24lPrede

12、fined Colormaps:EECE-5626: Color Image Processing16/24lColor images are acquired (or formed) via color cameras that predominantly use the RGB color model and employ CCD array sensors for each R, G, and B color.lHence it is reasonable to model a “noisy” color image as being formed by the correspondin

13、g “noisy” R, G, and B component images.lThe noisy image model then iswhere is an equivalent RGB color noise image while each noise variable, R, G, or B is an independent noise field. xnoisy=x+hxnoisy,Rxnoisy,Gxnoisy,B=xRxGxB+hRhGhBEECE-5626: Color Image Processing17/24lExample: Noise in RGB space ve

14、rsus HSV spacef = imread(imagedir,Fig0604(a)(iris).tif); % Load image figure; imshow(f); % Show imageg = imnoise(f,gaussian,0,0.1); % Add Noise in RGB spacefigure; imshow(g); % Show imagew = rgb2hsv(f); % RGB to HSV Conversionw = imnoise(w,gaussian,0,0.1); % Add Noise in HSV spacew = hsv2rgb(w); % H

15、SV to RGB conversionfigure; imshow(w); % Show imageOriginal ImageNoise in RGBNoise in HSVEECE-5626: Color Image Processing18/24lSpatial averaging or smoothing of monochrome images is accomplished by convolving with a mask which is symmetric (to avoid phase distortion problems).lThe process of smooth

16、ing a color image is formulated in a similar fashion, except that instead of single pixels we now have three pixels at each spatial location as shown below:EECE-5626: Color Image Processing19/24lLet Sm,n denote a neighborhood centered at (m,n) in a color image. Then the average of an RGB image is gi

17、ven bywhere K is the number of pixels in the neighborhood.lThus the averaging over a neighborhood can be carried out either on the color vector space or on the individual component basis. y(n1,n2) =x(k1,k2)K(k1,k2)Sn1,n2=1KxR(k1,k2)(k1,k2)Sn1,n2xG(k1,k2)(k1,k2)Sn1,n2xB(k1,k2)(k1,k2)Sn1,n2EECE-5626:

18、Color Image Processing20/24lSmoothing steps:1. Extract the three component images: fR = f(:,:,1); fG = f(:,:,2); fB = f(:,:,3); 2. Filter each components individually fR_filt = imfilter(fR,h); fG_filt = imfilter(fG,h); fB_filt = imfilter(fB,h);3. Reconstruct the filtered RGB image f_filtered = cat(3

19、,fR_filt,fG_filt,fB_filt);lOr, perform the entire operation in a vector fashion on the RGB image f_filtered = imfilter(f,h);EECE-5626: Color Image Processing21/24lExample: Smoothing in RGB space versus HSV spacef = imread(imagedir,Fig0604(a)(iris).tif); imshow(f); h = fspecial(average,11);g = imfilter(f,h,same);figure; imshow(g);w = rgb2hsv(f); w = imfilter(w,h,same); w = hsv2rgb(w); figure; imshow(w);Origi