color image processing - sharif university of...

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E. Fatemizadeh, Sharif University of Technology, 2011

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Digital Image Processing

Color Image Processing

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• Color Image Processing:

– Full-Color Processing:

• Color is acquired with a full-color sensor

– Pseudo-Color Processing:

• Assigning colors to monochrome images

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• Color Importance:

– Color is an excellent Descriptor:

• Suitable for object Identification and Extraction

– Discrimination:

• Humans can distinguish thousands of color shades and intensities But few shades of gray levels.

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• Color and Light:

– Light is fundamental of color vision • Achromatic light (BW Television): Intensity!

• Chromatic light (Color TV): Radiance, luminance, and Brightness

– Light is an Electromagnetic waves: • If wavelength (pure) is between ≈400nm and ≈700nm, the wave is

detectable by human eye and called a monochromatic (Not gray level!) light.

– λ<400nm: Ultraviolet

– λ >700nm: Infrared

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• What is Color:

– An Attribute of objects.

– Color Depends on:

• Spectral characterizations of light source (sun!) which illuminates the objects, PSD (Power Spectral Density)

• Spectral characteristics of objects (Reflectance)

• Spectral characteristics of sensors of imaging systems (ex. Eye)

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• Color Fundamentals:

– Physiopsychological phenomenon:

• How human brain perceive and interpret color? – Not yet fully understood

– Physical Phenomenon:

• Physical Nature of color is clear.

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• Newton Experiments (Spectral Analyzer)

– Emerging light is not longer white but a

continuous spectrum of color from violet to red

– Six broad region: violet, blue, green, yellow, orange, and red

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• Chromatic Light:

– Span the electromagnetic spectrum from 400 to 700 nm

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• Chromatic Light:

– Radiance:

• Total amount of energy that flows from light source (W)

– Luminance:

• The amount of energy that an observer perceives from light source. (IR case: R may be high while L is zero )

– Brightness:

• Subjective description, practically impossible to measure.

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• Radiance for Black-Body radiator (Plank’s law)

• For other sources similar curves exist.

1

5 2 0

( ) ( ) , Watts

exp 1

CC R C d

C

T

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• Luminance of objects:

Illumination source Object

reflection

C(λ)

R(λ)

S(λ)

Imaging Device

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• Luminance of objects:

visible region

L= ( ) , LumensC R S d

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• Brightness of objects:

– Subject dependent

– Human perception of brightness is related to c()

– The contributions that c(1) and c(2) make to human

perception of brightness are in general quite different for

1≠ 2 even though c(1) may be the same as c(2).

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• The relative luminous efficiency of a

monochromatic light is defined as c(r) / c (t)

when c(r) and c’(t) are matched in brightness.

– c(r) is the reference monochromatic light.

– r is 555 nm (yellow-green light) at which a typical

observer has maximum brightness sensitivity.

– c’(t) is a test monochromatic light.

• For this reference, the relative luminous efficiency

is always less than or equal to 1.

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• The relative luminous efficiency as a function of

is called the relative luminous efficiency function

and is denoted by v().

• Two monochromatic lights with c1(1) and c2(2)

appear equally bright to an observer when

– c1(1)v(1) = c2(2)v(2).

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• Eye as light sensor:

– (6-7) millions cons cell categorized in 3 group sensors: • Red (67%)

• Green (33%)

• Blue (2%)

– Blue is most sensitive

– Standard Definition of R-G-B (CIE): • Red: 700nm

• Green: 546.1 nm

• Blue: 435.8nm

– No single color may be called R,G, or B.

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• Relative Absorption of R/G/B cones:

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• Absorption characteristic of eye and Primary colors:

– Curves are experimental

– Colors are seen as variable combination of primary colors (R-G-B)

– With three specific primary colors (fixed wavelength) it is NOT possible to generate all spectrum colors.

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• Secondary Colors:

– Addition of primary colors:

• Magenta = Red + Blue

• Cyan = Green + Blue

• Yellow = Red + Green

– Mixing three primary and secondary with its opposite primary produce white color.

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• Primary Colors of light and primary colors of pigments (colorant):

– Primary color of pigments:

• Subtract or absorbs a primary color of light and reflect the other two!

– Primary colors of light are: R-G-B

– Primary color of pigments are: C-M-Y.

– Secondary color of light are: C-M-Y.

– Secondary color of pigments are: R-G-B

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• Primary Colors of light and primary colors of pigments (colorant):

– Problem is application dependent:

• R-G-B: Primary for Color TV

• C-M-Y: Primary for Color Printer.

Color of light: R G B

Color of pigments: absorb R absorb G absorb B Cyan Magenta Yellow

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• Color TV:

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Light

Pigments

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• Characteristics of color

– To distinguish a color from another

– Hue, Saturation, and Brightness

• Hue: Dominant color (wavelength) perceived by an observer. (Red, Orange, Yellow, ant etc.)

• Saturation: relative purity of color or the amount of white light mixed with a hue.

– Pure colors (δ(λ-λ0)) are fully saturated.

– Pink is less saturated.

• Brightness: chromatic notion of intensity.

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• Characteristics of color:

– Chromaticity:

• Hue+Saturation.

– Tri-stimulus:

• The amounts of R/G/B needed to form any particular color (X,Y, and Z)

BGR

Rr

BGR

Gg

BGR

Bb

1r g b r, g, b wavelength of light for that color

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• Chromaticity Diagram:

– Color composition as a function of red (x-axis) and green (y-axis).

– Blue = 1-Red-Green

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• Real and Imaginary Color – Negative Color

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1. Boundary are full saturated (Pure Color)

1b r g

2. Mixed Color

3. CIR White (1/3,1/3,1/3) -zero saturation

5. Color Mixing With various Value of 2 color

4. All shade Of Pure color

6. Color Mixing With various Value of 3 color

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– Color composition as a function of red (x-axis) and green (y-axis).

– Blue = 1-Red-Green

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– Not all color in chromaticity diagram are enclosed by a triangle!

• With three single primary color we can NOT have all possible color!

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Chromaticity Diagram

Monitor colors

Printers Inks

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• Color Model (Color Space, Color System)

– Specify colors in a standard way

– A coordinate system that each color is represented by a single point.

• Most used models:

– RGB model (Monitor/TV)

– CMY model (3-color Printers)

– CMYK model (4-color Printers)

– HSI model (Color Image Processing and Description)

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• RGB Color Model:

– Three Primary colors

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• RGB Color Model

– Pixel Depth: The number of bits used to represent each pixel in RGB space.

– Full-color image: 24-bit RGB color image.

• (R, G, B) = (8 bits, 8 bits, 8 bits)

• Number of colors: 3

82 16,777,216

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Color Image Generation

Color Image Acquisition

Filter-Sensor

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• Safe RGB Colors: – Subset of colors is enough for some application

– Safe RGB colors (safe Web colors, safe browser colors)

– Only 6 levels of each primary colors are used.

– 63=216

– 000000=Black

– 111111=White!

– 110000=Purest Red

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• Safe RGB Model

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• Safe RGB Colors

Safe RGB Full Color RGB

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• CMY and CMYK models: – CMY: Secondary colors of light, or primary colors

of pigments are used.

– Used to generate hardcopy output (Printer and Copier).

– Some facts: • Printer papers are white (reflect all colors)

• Printers use ink (Transparent)

• Cyan-Magenta-Yellow Pigments (ink) absorb Red-Green-Blue

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• CMY and CMYK models:

– Printers: Overlapping layers of varying percentages of transparent cyan, magenta, and yellow inks.

– Light is transmitted through the inks and reflects off the surface below them.

– The percentages of CMY ink subtract inverse percentages of RGB from the reflected light so that we see a particular color

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– White Paper reflect 100% of incoming light.

– K (Black) is practical problem of C+M+Y≠Black (Muddy Brown). Add a fraction of Black color

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• CMY to RGB:

• RGB to CMYK:

1 1

1 1

1 1

C R R C

M G G M

Y B B Y

255255 2551. 255

255255 255

2. min , , 255255

C KCC R K

M G M KM

Y B K

Y KK C M Y YK

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• HSI model:

– Human description of color is not RGB or CMYK

– Human description of color is Hue, Saturation and Brightness:

• Hue: color attribute

• Saturation: purity of color

• Brightness: achromatic notion of intensity

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• RGB to HIS conversion Intensity Line (Equal RGB value)

Iso-Hue Triangle

Saturation (Distance)

Intensity

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120º and 60º Distance (Pri. & Sec.)

Different Geometrical

Model is possible

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• Single Hue

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• Split Channel

Hue Saturation Intensity

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RGB Hue

Saturation Intensity

• Channel Split

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Hue saturation

intensity Reconstructed

• Modifying Channel

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• Split Channel (RGB Model)

RGB R G B

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• Split Channel (CMYK Model)

CMYK C M Y K

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• Pseudo-color image processing

– Assign colors to gray values based on a specified criterion

– For human visualization and interpretation of gray-scale events

• Methods:

– Intensity slicing

– Gray level to color transformations

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• Gray Level Slicing

1, , ,k k kf x y c f x y G G

Color 2

Color 1

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• Gray Level Slicing (Two Levels)

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• Gray Level Slicing (More Levels)

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• Example (Nuclear Imaging) with 8 levels

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• Industrial Application

– Welding

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• Example:

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• Gray Level to Color Transform

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• Example

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• Example:

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• Combine several monochrome images:

– multi-spectral images (Remote Sensing/Medical)

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R G

B Near IR

R-G-B IR-G-B

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• Using Knowledge

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• Full-Color Image Processing

– Process each color components individually

– Process color vector directly

,

, ,

,

R x y

x y G x y

B x y

c

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• Color Transform

– Gray Level Transform:

– Color Image Transform:

• n=3 for RGB/HSI/CMY

• n=4 for CMYK

, ,g x y T f x y

1 2, , , , 1,2, ,i i ns T r r r i n

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• Intensity Magnification: • HSI:

• RGB:

• CMY:

, ,g x y T f x y

3 3 2 2 1 1, ,s kr s r s r

i is kr

1i is kr k

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• Sample Image:

– CMYK

– RGB

– HSI

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• Intensity Adjustment (k=0.7)

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• Color Complements:

– Analogy of Gray-Level Negative

– Color Complement: Hue directly opposite one another on color circle.

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Two different

approaches!

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• Color Slicing:

– Highlight a specific range of colors.

• Set non desired color to gray level.

0.5 2

O.W.

i i

i

i

r a Ws

r

2 2

0

1

0.5

O.W.

n

i i

ji

i

r a Rs

r

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W=0.2549 R0=0.1765

• Examples:

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Light

Flat

Dark

• Tonal Correction

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• Color Balancing:

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• Histogram Processing:

– Histogram Equalization may NOT apply independently!

– Logical approach:

• Uniform Intensity

• Hue unchanged

• Saturation may be changed or not!

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original

Intensity Equlization

Saturation Increasing

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• Color Image Smoothing:

– Like as Gray Level Images!

,

,

,

,

1ˆ , ,

,

xy

xy

xy

x y S

x y S

x y S

R x y

x y G x yMN

B x y

C

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Co

lor

Imag

e

R-C

han

ne

l

G-C

han

nel

B-C

han

nel

• RGB Components

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• HSI Components

H-Channel S-Channel I-Channel

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RGB Smoothing I- Smoothing Difference

• Two approached for smoothing:

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• Color Image Sharpening:

2

2 2

2

,

, ,

,

R x y

x y G x y

B x y

c

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RGB Sharpening I- Sharpening Difference

• Two approached for Sharpening:

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• Color Image Segmentation:

Will be discuss later!

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• Segmentation in Vector Space

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• Example:

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• Color Image Edge Detection:

– Previous Operator is not defined for vector space!

– Problem is more complicated!

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One change result two changes

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– New Gradients vectors:

2 2 2

2 2 2

, , and :unit vectors in R, G, and B axes.

,

xx

yy

T

xy

R G B R G B

x x x y y y

R G Bg

x x x

R G Bg

y y y

R R G G B Bg

x y x y x y

T

T

r g b

u = r g b v = r g b

u u

v v

u v

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Direction of maximum rate of changes of c(x,y) is:

1

0.5

21tan

2

Rate of change:

1cos 2 2 sin 2

2

xy

xx yy

xx yy xx yy xy

g

g g

F g g g g g

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new

Sum of previous gradients for RGB channel

Diff.

• Example:

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R-Grad G-Grad B-Grad

• R-G-B Gradients:

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• Noise

– Model are same as before

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• R-G-B Channels Corruptions

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H-Channel S-Channel I-Channel

Noisy

Original

• HSI

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• G- Channel Corruption

– Effect on all

Channels of

HSI

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• Color Image Compression:

– Will be discuss later

color image processing - sharif university of...

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