Chapter 06
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Chapter 6 Color Image Processing
1
Preview • Color image processing is divided into two major areas – Full color – Pseudo color
2
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
3
Chapter 6 Color Image Processing
4
Chapter 6 Color Image Processing
5
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.
6
7
6.1 Color Fundamentals
• Primary colors – Red (R) – Green (G) – Blue (B)
• Secondary colors – Magenta (red + blue) – Cyan (green + blue) – Yellow (red +green)
8
6.1 Color Fundamentals
• Prime colors of light vs. Prime colors of pigments – Fig. 6.4
9
Chapter 6 Color Image Processing
10
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
11
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
12
Chapter 6 Color Image Processing
13
Chapter 6 Color Image Processing
14
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.
15
Chapter 6 Color Image Processing
16
Chapter 6 Color Image Processing
17
Chapter 6 Color Image Processing
18
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 19
Chapter 6 Color Image Processing
20
Chapter 6 Color Image Processing
21
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) 22
Chapter 6 Color Image Processing
23
24
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
25
Chapter 6 Color Image Processing
26
Chapter 6 Color Image Processing
27
Chapter 6 Color Image Processing
28
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
29
30
Chapter 6 Color Image Processing
31
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
32
Chapter 6 Color Image Processing
33
Chapter 6 Color Image Processing
34
Chapter 6 Color Image Processing
35
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
36
Chapter 6 Color Image Processing
37
Chapter 6 Color Image Processing
38
Chapter 6 Color Image Processing
39
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
40
Chapter 6 Color Image Processing
41
42
Chapter 6 Color Image Processing
43
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
44
Chapter 6 Color Image Processing
45
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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Chapter 6 Color Image Processing
48
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
49
Chapter 6 Color Image Processing
50
Chapter 6 Color Image Processing
51
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)
52
Chapter 6 Color Image Processing
53
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*)
54
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)
55
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Chapter 6 Color Image Processing
57
Chapter 6 Color Image Processing
58
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
59
Chapter 6 Color Image Processing
60
Chapter 6 Color Image Processing
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Chapter 6 Color Image Processing
62
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
63
Chapter 6 Color Image Processing
64
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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Chapter 6 Color Image Processing
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Chapter 6 Color Image Processing
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Chapter 6 Color Image Processing
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Chapter 6 Color Image Processing
70
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
71
Chapter 6 Color Image Processing
72
Chapter 6 Color Image Processing
73
Chapter 6 Color Image Processing
74
6.9 Color Image Compression – RGB – HSI – CMY(K) – Example 6.18 • JPEG 2000 image compression • Fig. 6.51
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Chapter 6 Color Image Processing
76
1
Preview • Color image processing is divided into two major areas – Full color – Pseudo color
2
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
3
Chapter 6 Color Image Processing
4
Chapter 6 Color Image Processing
5
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.
6
7
6.1 Color Fundamentals
• Primary colors – Red (R) – Green (G) – Blue (B)
• Secondary colors – Magenta (red + blue) – Cyan (green + blue) – Yellow (red +green)
8
6.1 Color Fundamentals
• Prime colors of light vs. Prime colors of pigments – Fig. 6.4
9
Chapter 6 Color Image Processing
10
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
11
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
12
Chapter 6 Color Image Processing
13
Chapter 6 Color Image Processing
14
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.
15
Chapter 6 Color Image Processing
16
Chapter 6 Color Image Processing
17
Chapter 6 Color Image Processing
18
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 19
Chapter 6 Color Image Processing
20
Chapter 6 Color Image Processing
21
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) 22
Chapter 6 Color Image Processing
23
24
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
25
Chapter 6 Color Image Processing
26
Chapter 6 Color Image Processing
27
Chapter 6 Color Image Processing
28
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
29
30
Chapter 6 Color Image Processing
31
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
32
Chapter 6 Color Image Processing
33
Chapter 6 Color Image Processing
34
Chapter 6 Color Image Processing
35
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
36
Chapter 6 Color Image Processing
37
Chapter 6 Color Image Processing
38
Chapter 6 Color Image Processing
39
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
40
Chapter 6 Color Image Processing
41
42
Chapter 6 Color Image Processing
43
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
44
Chapter 6 Color Image Processing
45
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
46
47
Chapter 6 Color Image Processing
48
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
49
Chapter 6 Color Image Processing
50
Chapter 6 Color Image Processing
51
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)
52
Chapter 6 Color Image Processing
53
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*)
54
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)
55
56
Chapter 6 Color Image Processing
57
Chapter 6 Color Image Processing
58
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
59
Chapter 6 Color Image Processing
60
Chapter 6 Color Image Processing
61
Chapter 6 Color Image Processing
62
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
63
Chapter 6 Color Image Processing
64
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
65
66
Chapter 6 Color Image Processing
67
Chapter 6 Color Image Processing
68
Chapter 6 Color Image Processing
69
Chapter 6 Color Image Processing
70
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
71
Chapter 6 Color Image Processing
72
Chapter 6 Color Image Processing
73
Chapter 6 Color Image Processing
74
6.9 Color Image Compression – RGB – HSI – CMY(K) – Example 6.18 • JPEG 2000 image compression • Fig. 6.51
75
Chapter 6 Color Image Processing
76
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