1 Color Image Processing Image Processing with Biomedical Applications ELEG-475/675 Prof. Barner Image Processing Color Image Processing Prof. Barner, ECE Department, University of Delaware 2 Color Image Processing Full-color and pseudo-color processing Color vision Color space representations Color processing Correction Enhancement Smoothing/sharpening Segmentation Image Processing Color Image Processing Prof. Barner, ECE Department, University of Delaware 3 Color Fundamentals (I) The visible light spectrum is continuous Six Broad regions: Violet, blue, green, yellow, orange, and red Object color depends on what wavelengths it reflects Achromatic light is void of color (flat spectrum) Characterization: intensity (gray level) Image Processing Color Image Processing Prof. Barner, ECE Department, University of Delaware 4 Color Fundamentals (II) Chromatic light spectrum: 400-700 nm Descriptive quantities: Radiance – total energy that flows from a light source (Watts) Luminance – amount of energy and observer perceives from a light source (lumens) Brightness – subjected descriptor of intensity
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Color Image Processing
Image Processing with Biomedical Applications
ELEG-475/675Prof. Barner
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 2
Color Image Processing
Full-color and pseudo-color processingColor visionColor space representationsColor processing
Radiance – total energy that flows from a light source (Watts)Luminance – amount of energy and observer perceives from a light source (lumens)Brightness – subjected descriptor of intensity
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Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 5
VisionResponse
Cone response:6-7 million receptorsRed sensitive: 65%Green sensitive: 33%Blue sensitive: 2%
Most sensitive receptors
Primary colors: red (R), green (G), blue (B)International Commission on Illumination (CIE) standard definitions:
Blue (435.8 nm), Green (546.1 nm), Red (700 nm)Defined in 1931 – doesn’t exactly match human perception
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 6
Primary and Secondary Colors
Add primary colors to obtain secondary colors of light:
Magenta, cyan, and yellowPrimarily colors of:
Light – sourcesRed, green, blue
Pigments – absorbs (subtracts) a primary color of light and reflects (transmits) the other two
Prof. Barner, ECE Department, University of Delaware 16
The HSI Color Space (I)
Hue, saturation, intensityhuman perceptual descriptionsof colorDecouples intensity (gray level)from hue and saturationRotate RGB cube so intensity is the vertical axis
The intensity component of any color is its vertical componentSaturation – distance from vertical axis
Zero saturation: colors (gray values) on the vertical axisFully saturated: pure colors on the cube boundaries
Hue – primary color indicated as an angle of rotation
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Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 17
The HSI Color Space (II)
View the HSI space from top down
Slicing plane perpendicular to intensity
Intensity – height of slicing planeSaturation –distance from center (intensity axis)Hue – rotation angle from RedNatural shape: hexagon
Normalized to circle or triangle
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 18
RGB to HSIConversion
Common HSI representationsRGB to HSI conversion
Result for normalized (circular) HSI representationTake care to note which HSI representation is being used!
{ if 360 if
B GB GH θ
θ≤
− >=
[ ]1
12 2
1 ( ) ( )2cos
( ) ( )( )
R G R B
R G R B G Bθ −
⎧ ⎫− + −⎪ ⎪= ⎨ ⎬
⎪ ⎪⎡ ⎤− + − −⎣ ⎦⎩ ⎭
[ ]31 min( , , )( )
S R G BR G B
= −+ +
1 ( )3
I R G B= + +
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 19
HSI to RGB Conversion – Three Cases
Case 1: RG sector (0°≤H≤120°)
Case 2: GB sector (120°≤H240°)
Case 3: BR sector (240°≤H≤360°)
(1 )B I S= −
cos1cos(60 )
S HR IH
⎡ ⎤= +⎢ ⎥° −⎣ ⎦
1 ( )G R B= − +
120H H= − °
(1 )R I S= −
cos1cos(60 )
S HG IH
⎡ ⎤= +⎢ ⎥° −⎣ ⎦
1 ( )B R G= − +
240H H= − °
(1 )G I S= −
cos1cos(60 )
S HB IH
⎡ ⎤= +⎢ ⎥° −⎣ ⎦
1 ( )R G B= − +
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 20
HSI Component Example (I)
HSI representations of the color cubeNormalized values represented as gray valuesOnly values on surface of cube shown
Explain:Sharp transition in hueDark and light corners in saturationUniform intensity
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Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 21
HSI Component Example (II)
Primary and secondary colors
HSI representation
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 22
Pseudocolor Image Processing
Assigning colors to gray values yields Pseudocolor(false color) images
assignment criteria is application-specific
Intensity (density) slicingAssign colors based on gray value relation to slicing plane
Special case: Thresholding
( , ) if ( , )k kf x y c f x y V= ∈
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 23
Density Slicing Example (I)
Eight color density slicing of thyroid PhantomDensity slicing enables visualization of variations and details
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 24
Density SlicingExample (II)
X-ray image of a weld
Density slicing to help visualize cracks
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Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 25
Density Slicing Example (III)
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 26
Gray Level to Color Transformations
Each color can be a dependent/independent function of gray level
Example: RGB processingGoal: highlight (color) objects or features of interest
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 27
Example: Airport x-ray Scanning System
Sinusoidal color mappings
Phase changes between components yield different resultsGreatest color changes at sinusoidal troughs
Largest derivative
First mapping:Highlights explosives
Second mapping:Explosives and bag have similar mappings
Explosive is “transparent”
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 28
Multispectral Extensions
Pseudo coloring is often used in the visualization of multispectral images
Examples: Satellite and astronomy imagesVisible spectrum, infrared, radio waves, etc.
Transformations are applications and spectral band dependent
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Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 29
Wash. DC LANDSAT Example (I)
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 30
Wash. DC LANDSATExample (II)
Images in bands 1-4Color composite image using
Band 1 (visible blue) as blueBand 2 (visible green) as greenBand 3 (visible red) as redResult is difficult to analyze
Color composite image usingBands 1 and 2 as aboveBand 4 (near infrared) as redBetter distinguishes between biomass (red dominated) and man-made structures
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 31
Galileo SpacecraftExample
Multispectral image ofJupiter’s moon: ItoMultispectral bands are chemical composition sensitive
Pseudocolor imageHighlights volcanic activity
New deposits: redOld deposits: yellow
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 32
Full-Color Image Processing
Samples inobservation window
Vectors
General transformation:
Restrict transformation to be a set {T1,T2, …,Tn} of transformations or color mappings
RGB: n=3; HSI: n=3; CMYK: n=4
( , ) ( , )( , ) ( , ) ( , )
( , ) ( , )
R
G
B
c x y R x yc x y c x y G x y
c x y B x y
⎡ ⎤ ⎡ ⎤⎢ ⎥ ⎢ ⎥= =⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥⎣ ⎦ ⎣ ⎦
[ ]( , ) ( , )g x y T f x y=
1 2( , ,..., )i i ns T r r r=
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Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 33
Image &Components
Image and CMYK, RGB, and HSI componentsSimple application:
Intensity scalingHSI space:
s3=kr3
RGB space:si=kri i=1,2,3
CMY space:si=kri +(1-k) i=1,2,3
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 34
Scaling Result
Scaling result for k=0.7Shown: RGB, CMY, and HSI transformations
(HS and I transformations swapped)
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 35
Color Complements
Color circleCircular connection of visible spectrum
Color complementationColor negativesShown transformations
RGB: exactHSI: approximation
S component not independent of H&I
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 36
Color Management Systems (CMS)
All devices have their own profileGoal: device independent color model
Must be able to represent the entire color gamutShown:
RGB monitor gamutFull gamut
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Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 37
CIE L*a*b* Color Space (I)
Desired color space attributesColor metric – colors perceived as matching are identically codedPerceptually uniform – color differences among various hues areperceived uniformly
Distance in colorspace matchesperceived differencein colors
Device independent –independent of specific device displaycharacteristics
Gamut encompasses entire visible spectrum
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 38
CIE L*a*b* Color Space (II)
Tristimulus to L*a*b* conversion:
where
Reference white tristimulusvalues:
XW=0.3127, YW=0.3290, and ZW=1-XW-YW
Components:Intensity (lightness): L*Color:
Red minus green: a* Green minus blue: b*
Appropriate for applications that require:
Full color space representationColor space distance and perceptual difference matchingDrawbacks: computational cost
*
*
*
116 16
500
200
W
W W
W W
YL hY
X Ya h hX Y
Y Zb h hY Z
⎛ ⎞= ⋅ −⎜ ⎟
⎝ ⎠⎡ ⎤⎛ ⎞ ⎛ ⎞
= −⎢ ⎥⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠⎣ ⎦
⎡ ⎤⎛ ⎞ ⎛ ⎞= −⎢ ⎥⎜ ⎟ ⎜ ⎟
⎝ ⎠ ⎝ ⎠⎣ ⎦
( ) { 3 0.0088567.787 16/116 0.008856
q qq qh q >+ ≤=
Image ProcessingColor Image Processing
Prof. Barner, ECE Department, University of Delaware 39