6.1 color fundamentals digital image processing 6.2 color...
TRANSCRIPT
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Digital Image Processing
Jen-Hui Chuang
Department of Computer ScienceNational Chiao Tung University
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6 Color Image Processing6.1 Color Fundamentals 6.2 Color Models 6.3 Pseudocolor Image Processing 6.4 Basics of Full-Color Image Processing 6.5 Color Transformations 6.6 Smoothing and Sharpening 6.7 Image Segmentation Based on Color 6.8 Noise in Color Images 6.9 Color Image Compression
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Color Spectrum
6.1 Color Fundamentals
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6.1 Color Fundamentals
Spectrum of Electromagnetic Waves
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6.1 Color Fundamentals
Three basic quantities to describe the quality of a chromatic light source:
Radiance — … energy … light source
Luminance — … energy … observer
Brightness —… a subjective descriptor …
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Absorption of light by cones in human eye
Primary colors (CIE):
R: 700 nmG: 546.1 nmB: 435.8 nm
2% 32% 65%
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Primary and secondary colors
Trisitimulus values: X, Y, and Z
Trichromatic coefficients:
x = X / (X + Y + Z)y = Y / (X + Y + Z)z = Z / (X + Y + Z)
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Chromaticity Diagram
88888
z = 1 – x – y
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Typical color gamut of color monitors andcolor printing devices
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6.2 Color Models6.2.1 The RGB Color Model
Ex. 6.1 The RGB color cube
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Ex. 6.1 The RGB color cube (cont.)
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Ex. 6.1 The RGB color cube (cont.)
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The RGB safe-color cube
11111111111111133 14
6.2.2 The CMY and CMYK Color Models
6.2.3 The HSI Color ModelConceptual relationship between RGB and HSI models
C = 1 – RM = 1 – G subtractive Y = 1 – B
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Hue, Saturation and Intensity
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Hue, Saturation and Intensity
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GBif-360GBif
H
})])(()[(
)]()[(21
{cos 2/121
BGBRGR
BRGR
)],,[min(31 BGRBGR
S
)(31 BGRI
= 1 – min(R,G,B)/I
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Ex. 6.2 HSI values and the RGB color cube
H S I
)(31 BGRIS = 1 – min(R,G,B)/IH
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Manipulating HSI component images
?
?
?
H
S I
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Manipulating HSI component images (cont.)
H S
I20
6.3 Pseudocolor Image Processing
6.3.1 Intensity Slicing
222222222222222222222222222222222000000000
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6.3.1 Intensity Slicing
Ex. 6.3
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Ex. X-ray image of a weld
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Ex. 6.4 Use of color to highlight rainfall levels
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Ex. 6.4 Use of color to highlight …
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Ex. 6.4 Use of color to highlight …
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6.3.2 Gray Level to Color Transformations
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Ex. 6.5 Use of pseudocolor to highlight explosives
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Color coding of multispectral images
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Ex. 6.6 Color coding of multispectral images
222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222299999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999R1
G
B
R2
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Ex. 6.6 (cont.)
yellow: old sulfur depositsred: new from volcano
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6.4 Basics of Full-Color Image Processing
Per-color-component and vector-based processingif two conditions are satisfied
Ex. Neighborhood averaging
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6.5 Color Transformations6.5.1 Formulation
Ex. A full-color image and its various color space components
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Ex. Adjusting the intensity of a color image
K = 0.7
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6.5.2 Color Complements
Color Circle
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Ex. 6.7 Computing color image complements
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6.5.3 Color SlicingEx. 6.8
36Volume: 0.0166 : 0.0173
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6.5.4 Tone and Color Corrections
Color Management Systems (CMS)
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6.5.4 Tone and Color Corrections
Device-independent color model: CIE LAB
)]()([500*
)]()([500*
16)(116*
WW
WW
W
ZZh
YYhb
YYh
XXha
YYhL
where
008856.0116/16787.7008856.0)(
3
qqqqqh
XW , YW , ZW : reference white. (e.g., x = 0.3127, y = 0.3290 in Fig. 6.5)
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6.5.4 Tone and Color CorrectionsEx. 6.9 Tonal transformations
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6.5.4 Tone and Color CorrectionsEx. 6.9 Tonal transformations (cont)
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Ex. 6.10 Color balancing
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6.5.5 Histogram ProcessingEx. 6.11 Histogram equalization in the HSI color space
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6.6 Smoothing and Sharpening6.6.1 Color Image SmoothingEx. 6.12 Color image smoothing by neighborhood averaging
R
G Bxy
xy
xy
Syx
Syx
Syx
yxBK
yxGK
yxRK
yx
),(
),(
),(
),(1
),(1
),(1
),(
4444444444444444
Ex. 6.12 (cont.)
H S I
(Smoothing I only)(Smoothing R, G, &B)
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Ex. 6.13 Sharpening with the Laplacian
6.6.2 Color Image Sharpening
(Sharpening I only)(Sharpening R, G, &B)
),(),(),(
)],([2
2
2
2
yxByxGyxR
yx
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6.7 Color Segmentation6.7.1 Segmentation in HSI Color Space
H
S I
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6.7.2 Segmentation in RGB Vector Space
Three Approaches
Ex. 6.15 Color image segmentation in RGB Space
R, G, B: 1.2548
6.7.3 Color Edge DetectionGradient Operator for Vector Quantities ?
R G B
R G B
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Ex. 6.16 Edge detection in vector space
(6.7-9)
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Ex. 6.16 (cont.)
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R G B
515555555555555555555555555555555555555555555555555555555555111111111111111111111111111111111111111111111111111111111111111
6.8 Noise in Color Images
Ex. 6.17 Effects of converting noisy RGB
images to HSI
R G
BN(0, 800)
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6.8 Noise in Color Images
Ex. 6.17 (cont.)
H S I
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Ex. 6.17 (cont.)
H
S I
“Green” corrupted(S & P)
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6.9 Color Image Compression
1:230or
1min : 4hr
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