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Transcript of Chapter 06
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Chapter 6Color Image Processing
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Preview• Color image processing is divided into
two major areas– Full color– Pseudo color
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6.1 Color Fundamentals
• The Color spectrum may be divided into six broad regions. (1666)– As Fig 6.1 shows
• Visible light is composed of a relative narrow band of frequencies.– As illustrated in Fig 6.2
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6.1 Color Fundamentals
• For human eye – Approximately 65% of all cone are
sensitive to red light– 33% are sensitive to green light– 2% are sensitive to blue light
• But blue cones are the most sensitive.– Fig 6.3 shows average experimental
curves detailing the absorption of light by the red, green, and blue cones in the eye.
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6.1 Color Fundamentals
• Primary colors– Red (R)– Green (G)– Blue (B)
• Secondary colors– Magenta (red + blue)– Cyan (green + blue)– Yellow (red +green)
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6.1 Color Fundamentals
• Prime colors of light vs. Prime colors of pigments– Fig. 6.4
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6.1 Color Fundamentals
• Another color characteristics– Brightness
• Chromatic notation of intensity– Hue
• Dominant wavelength in a mixture of light waves
– Saturation• The mount of white light mixed with hue
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6.1 Color Fundamentals
• Chromaticity – Hue and Saturation• CIE chromaticity diagram
– Fig. 6.5• Typical range of colors (color gamut 色階)
produced by RGB monitors– Fig. 6.6
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6.2 Color Models
• Color model also called– Color space– Color system
• The RGB Color Model– The color subspace of interest is the cube
shown in Fig. 6.7.– Fig. 6.8 - RGB 24-bit color cube– A color image can be acquire by using
three filters – Fig. 6.9.
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6.2 Color Models
• The set of safe RGB colors – Many systems in the use today are limited
to 256 colors.– Also call the set of all-system-safe colors– In Internet application – safe Web colors or
safe browser colors– 40 colors are processed differently by
various O.S.– 216 colors – each RGB value can only be
0, 51, 102, 153, 204, or 255. (63 = 216) – Table 6.1, Fig. 6.10, and 6.11
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6.2 Color Models
• The CMY and CMYK Color Models– RGB to CMY – Eq. 6.2-1 (pp.294)– K – is the added color, black
• The HIS Color Model – Fig 6.13 and 6.14– When human view a color object
• Hue (H)• Saturation (S)• Brightness or intensity (I)
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6.2 Color Models
• Converting color from RGB to HSI– Eq. 6.2-2 ~ Eq. 6.2-4
• Converting color from HSI to RGB– Eq. 6.2-5 ~ Eq. 6.2-15
• Example 6.2 – The HIS values corresponding to the
image of the RGB color cube – Fig. 6.15.• Manipulating HIS component image
– Fig. 6.16 and 6.17
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6.3 Pseudo-color Image Processing
• Also called false color• Intensity slicing
– Assigned different color to each side of the intensity plane
– Fig. 6.18– Fig. 6.19
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6.3 Pseudo-color Image Processing
• Example 6.3– Medical monochrome image - Fig. 6.20– X-ray monochrome image - Fig. 6.21
• Example 6.4– Use of color to highlight rainfall levels– Fig. 6.22
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6.3 Pseudo-color Image Processing
• Gray level to color transformations– Functional block diagram for pseudo-color
image processing• Fig. 6.23
– Example 6.5• Use of pseudo-color for highlighting explosive
contained in luggage.• Fig. 6.24• Transformation function is shown in Fig. 6.25
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6.3 Pseudo-color Image Processing
– Combine several monochrome image into a single color composite
• Fig. 6.26– Example 6.6
• Color coding of multi-spectral images• Fig. 6.27 – ground• Fig. 6.28 – Jupiter Moon Io
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6.4 Basics of full-color image processing
– Divide a pixel in an color image into three components – Eq. 6.4-2
– Using spatial masks for RGB color images• Fig. 6.29
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6.5 Color Transformations
– Same as the gray-level transformation techniques of Chapter 3
– Use color mapping functions for each color component
• See Eq. 6.5-2 (pp.315)• Color-space components – Fig 6.30• Adjusting the intensity of an image using color
transformations – Fig. 6.31
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6.5 Color Transformations
– Color Complements• The Hues directly opposite one another on the
color circle• Fig. 6.32• Example 6.7
– Computing color image complements.– Fig. 6.33
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6.5 Color Transformations
– Color Slicing• Useful for separating objects from surroundings• Example 6.8
– Fig. 6.34
– Tone and Color Corrections• Digital darkroom• Need a device independent color model
– CIE L*a*b*– Eq. 6.5-9 ~ Eq. 6.5-12 (pp.322)
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6.5 Color Transformations
– L*a*b* color space is• Colorimetric • Perceptually uniform• Device independent• Not a directly displayable format
– L*a*b* color components• Lightness (L*)• Red – green (a*)• Green – blue (b*)
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6.5 Color Transformations
• Example 6.9– Tonal transformations– Fig. 6.35
• Example 6.10– Color balancing– Fig. 6.36
– Histogram Processing• Histogram equaliztaion
– Example 6.11 – in HIS color space (Fig. 6.37)
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6.6 Smoothing and Sharpening
– Color image smoothing• In the RGB color images
– Eq. 6.6-1 and 6.6-2 (pp. 328)
• Example 6.12 – color image smoothing by neighboring averaging– Fig 6.38 and Fig. 6.39– Difference image is shown in Fig. 6.40
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6.6 Smoothing and Sharpening
– Color image sharpening• In the RGB color images
– Eq. 6.6-3 (pp. 330)
• Example 6.13 – color image sharpening the Laplacian– Fig 6.41
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6.7 Color Segmentation
– Segmentation in HSI Color Space• Example 6.14
– Fig. 6.42
– Segmentation in RGB Color Space• Example 6.15
– Fig. 6.44
– Color Edge Detection• Consider Fig. 6.45• Example 6.16 – RGB color image
– Fig. 6.46 and 6.47
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6.8 Noise in Color Image
– Usually, the noise content of a color image has the same characteristics in each color channel.
– Example 6.17• Illustration of the effects of converting noisy
RGB images to HSI• Fig. 6.48, 6.49, and 6.50
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6.9 Color Image Compression
– RGB– HSI– CMY(K)
– Example 6.18• JPEG 2000 image compression• Fig. 6.51
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