Digital Image Processing Chapter 6: Color Image Processing ( )

Transcription

1 Digital Image Processing Chapter 6: Color Image Processing ( )

2 6. Preview The process followed by the human brain in perceiving and interpreting color is a physiopsychological henomenon that is not yet fully understood, the physical nature of color can be expressed on a formal basis supported by experiment and theoretical results. 2

3 6.1 Color Fundamentals The process followed by the human brain in perceiving and interpreting color is a physiopsychological henomenon that is not yet fully understood, the physical nature of color can be expressed on a formal basis supported by experiment and theoretical results. 3

4 6. Color Image Processing In 1666, Sir Isaac Newton discovered that when a beam of sunlight passes through a glass prism, the emerging beam of light us not white but consist in stead of a continuous spectrum of colors ranging from violet at one end to red at the other. six broad regions : violet, blue, green, yellow, orange, and red 4

5 6. Color Image Processing Visible light is composed of a relatively narrow band of frequencies in the electromagnetic spectrum 5

6 6. Color Image Processing average experimental curves detailing the absorption of light by the reg, green, blue cones in the eye 6

7 Three basic quantities are used to describe the quality of a chromatic light source: radiance, luminance, and brightness. Radiance is the total amount of energy that flow from the light source, and it is usually measured in watts (W). Luminance, measured in lumens (lm), gives a measure of the amount of energy an observer perceives from a light source. Brightness is a subjective descriptor that is practically impossible to measure. It embodies the achromatic notion of intensity and is one of the key factors in describing color sensation. 7

8 6. Color Image Processing three primary colors and colors of pigments and their combinations to produce the second colors 8

9 6.1 Color Fundamentals The characteristics generally used to distinguish one color from another are brightness, hue, and saturation. -Hue : attribute associated with the dominant wavelength in a mixture of light waves. -saturation : relative puriry or the amount of white light mixed with a hue. The amounts of red, green, blue needed to form any particular color are called the tristimuus values and are denoted X, Y, Z x = X X + Y + Z y = Y X + Y + Z z = Z X + Y + Z x + y + z = 1 9

10 6. Color Image Processing another approch for specifying colors is to use the CIE chromaticity diagram, which shows color compositin as a function of x(red) and y(green), z(blue). 10

11 6. Color Image Processing the triangle shows a typical range of colors produced by RGB monitors. 11

12 The RGB Color Model RGB primary values (Red, Green, Blue) Secondary colors (Cyan, Magenta, Yellow) All of RGB range : [0, 1] Each of RGB have a 8-bit Pixel depth RGB triplet have a 24-bit Pixel depth (1, 1, 1) Total number of colors (2 8 ) 3 = 16,777,216 (0, 0, 0) Gray Scale (It often denote Full Color Image)

13 The RGB Color Model Three individual component images (Gray scale images) Color Moniter One of surface plane of Fig 6.8

14 The RGB Color Model Safe RGB Colors (Safe Web Colors or All Systems Safe Colors) It means subset of colors that Variety of systems reproduced faithfully independently of hardware Each of RGB values can only 0, 51, 102, 153, 204, 255 (Tab 6.1) (RGB triplet have 6 3 = 216)

15 The CMY and CMYK Color Model Cyan, Magenta, and Yellow - Secondary Colors of Light - Primary Values of Pigments (It absorb opposite color) RGB to CMY Transform C 1 R M = 1 - G Y 1 B (All of colors range : [0, 1]) - Ex) Pure Megenta not reflect Green CMYK Color - C + M + K = muddy looking Black (so add a fourth color, Black)

16 6.2.3 The HSI Color Model HSI(Hue, Saturation, intensity) Hue : 색상 Saturation : 채도 Intensity : 명도 / 밝기 HSI Color Model decouples the intensity component from the color-carrying information(hue and saturation) in a color image. The HSI color model is an ideal tool for developing image processing algorithms based on color descriptions that are natural and intuitive to humans.

17 6.2.3 The HSI Color Model

18 6.2.3 The HSI Color Model

19 6.2.3 The HSI Color Model

20 6.2.3 The HSI Color Model (6.2-2) G B G B H / )] )( ( ) [( )] ( ) [( cos B G B R G R B R G R )],, [min( ) ( 3 1 B G R B G R S ) ( 3 1 B G R I Converting colors from RGB to HSI (6.2-3) (6.2-4)

21 6.2.3 The HSI Color Model Converting colors from HSI to RGB RG Sector (0 H 120 ) B I( 1 S) (6.2-5) R I[1 S cos H cos(60 H ] ) (6.2-6) G 3I ( R B) (6.2-7)

22 6.2.3 The HSI Color Model Converting colors from HSI to RGB GB Sector (120 H 240 ) H H 120 (6.2-8) R I( 1 S) (6.2-9) G I[1 S cos H cos(60 H ] ) (6.2-10) B 3I ( R G) (6.2-11)

23 6.2.3 The HSI Color Model Converting colors from HSI to RGB BR Sector (240 H 360 ) H H 240 (6.2-12) G I( 1 S) (6.2-13) B I[1 S cos H cos(60 H ] ) (6.2-14) R 3I ( R G) (6.2-15)

24 6.2.3 The HSI Color Model Example 6.2 The HSI values corresponding to the image of the RGB color cube

25 6.2.3 The HSI Color Model Manipulating HSI component images

26 6.2.3 The HSI Color Model Manipulating HSI component images

27 6.3 Pseudocolor Image Processing The pseudocolors used to colorize a gray level image do not re present the original, true colors if there were originally a color i mage. The principal use of pseudocolor is for human visualization and interpretation of gray-scale events in an image or sequence of i mages.

28 6.3.1 Intensity Slicing Intensity slicing and colour coding is one of the simplest kinds of pseudocolour image processing Let [0, L-1] represent the grey scale Let l 0 represent black [f(x, y) = 0] and let l L-1 represent white [f(x, y) = L-1] Suppose P planes perpendicular to the intensity axis are defined at levels l 1, l 2,, l p Assuming that 0 < P < L-1, the P planes partition the gray scale into P + 1 intervals V 1, V 2,,V P+1 (6.3-1) where C k is the colour associated with the k th intensity level V k defined by the partitioning planes at l = k 1 and l = k f (x,y) c k if f (x,y) V k

29 6.3.1 Intensity Slicing A gray scale image viewed as a 3D surface. FIGURE 6.18 Geometric interpretation of the intensity-slicing technique.

30 6.3.1 Intensity Slicing FIGURE 6.19 An alternative representation of the intensity-slicing technique.

31 6.3.1 Intensity Slicing (a) (b) An X-ray image of the Picker Thyroid Phantom. After density slicing into 8 colors FIGURE 6.20 (a) Monochrome image of the Picker Thyroid Phantom. (b) Result of density slicing into eight colors. (Courtesy of Dr. J. L. Blankenship, Instrumentation and Controls Division, Oak Ridge National Laboratory.)

32 6.3.1 Intensity Slicing (a) (b) An X-ray image of a weld with cracks After assigning a yellow color to pixels with value 255 and a blue color to all other pixels. FIGURE 6.21 (a) Monochrome X-ray image of a weld. (b) Result of color coding. (Original image courtesy of X-TEK Systems, Ltd.)

33 6.3.1 Intensity Slicing FIGURE 6.22 (a) (b) (a) Gray-scale image in which intensity (in t he lighter horizontal band shown) corres ponds to average m onthly rainfall. (b) Colors assigned to in tensity values. (c) Color-coded image. (d) Zoom of the South A merica region. (c) Gray-scale image (d) (Courtesy of NASA.) Color-coded image

34 6.3.2 Intensity Color Transformations Other types of transformations are more general and thus are capable of achieving a wider range of pseudocolor enhancement results than the simple slicing technique discussed in the preceding section.

35 6.3.2 Intensity Color Transformations Assigning colors to gray levels based on specific mapping functions FIGURE 6.23 Functional block diagram for pseudocolor image processing. f R, f G, and f B are fed into the corresponding red, green, and blue inputs of an RGB color monitor.

36 6.3.2 Intensity Color Transformations An X-ray image of a garment bag (a) An X-ray image of a garment bag with a simulated explosive device (b) (c) Color coded images FIGURE 6.24 Pseudocolor enhancement by using the gray-level to color transformations in Fig (Original courtesy of Dr. Mike Hurwitz, Westinghouse.)

37 6.3.2 Intensity Color Transformations FIGURE 6.25 Transformation fu nctions used to o btain the in Fig image (b) (c)

38 6.3.2 Intensity Color Transformations Used in the case where there are many monochrome images such as multispectral satellite images. (b) (c) FIGURE 6.26 A pseudocolor coding approach used when several monochrome image are available.

39 Ex.6.6 Color coding of multispectral images The first three images are in the visible red, green and blue and the fourth is in the near infrared(see Table 1.1 and Fig. 1.10). Figure 6.27(e) is the full-color image obtained by combining the first three images into an RGB image. Full-color images of dense areas are difficult to interpret, but one notable feature of this image is the difference in color in various parts of the Potomac River. Figure 6.27(f) This image was formed by replacing the red component of Fig. 6.27(e) with the near-infrared image.

40 Ex. 6.6 Color coding of multispectral images These are images of the Jupiter moon Io, shown in pseudocolor by combining several of the sensor images from the Galileo spacecraft, some of which are in spectral regions not visible to the eye. However, by understanding the physical and chemical processes likely to affect sensor response, it is possible to combine the sensed images into a meaningful pseudocolr map. One way to combine the sensed image data is by how they show either differences in surface chemical composition or changes in the way the surface reflects sunlight.