No Slide TitleBrightness – Achromatic notion of intensity. Hue – Dominant wavelength in the mixture of light waves, i.e., dominant color perceived by observer, say, Red Saturation

EEE 6209 – Digital Image ProcessingEEE 6209 – Digital Image Processing

© Dr. S. M. Mahbubur Rahman

Color Image ProcessingColor Image Processing

OutlineMotivation and Color Fundamentals

Standard Color Models (RGB/CMYK/HSI)

Demosaicing and Color Filtering

Pseudo-color and Full-color Image Processing

Color Transformation

Tone and Color Corrections

Color Image Smoothing, Sharpening, Segmentation, Edge detection, and Denoising

Assignments




MotivationColor is a powerful descriptors that simplifies feature extraction

and object identification from a sceneHVS is sensitive to thousands of color shades or intensities

instead of two dozens of shades of gray Basics

Color spectrum of visible light has six broad regions, viz., Violet, Blue, Green, Yellow, Orange, and Red (ViBGYOR)

Achromatic light is void of color. Chromatic light spans electromagnetic spectrum from 400 nm to 700 nm.

Radiance is the amount of light energy radiates from light source in Watts. Luminance is the energy perceives by viewer in Lumen. Infrared spectrum may have significant radiance but zero luminance

Brightness is a subjective measure, depends on the reflectance or absorption characteristics of the observed body




Color Spectrum

Color spectrum of white light passing through a prism. Experimented by Sir Isaac Newton in 1666




Absorption Characteristics

65% cones are sensitive to Red

33% cones are for Green

2% cones are for Blue, but very sensitive!

Absorption characteristics experimented in 1965.




Primary and Secondary ColorsIn 1931, CIE (Commission Internationale de l’Ecairage –

International Commission on Illumination) designated three primary color : Blue = 435.8 nm, Green = 546.1 nm, and Red = 700 nm long before absorption characteristics are obtained.

Additive mixing of primary color lights provides secondary color of lights: Magenta= Red+Blue, Cyan= Green+Blue, Yellow=Red+Green.

Additive mixing primary color lights is used as principle of modern display devices. CRT (cathode ray tube) uses electron sensitive phosphor. LCD (liquid crystal displays) uses thin filmtransistors (TFTs) to block or pass polarized light. In plasma units pixels are tiny gas cells coated with phosphor.




Pigments act like subtractive colors, and hence mixtures of pigments provide common primary color of light. For example, Magenta and Yellow provides Red color.

Primary and Secondary Colors




Characteristics of ColorBrightness – Achromatic notion of intensity.

Hue – Dominant wavelength in the mixture of light waves, i.e., dominant color perceived by observer, say, Red

Saturation – Relative purity or the amount of white light mixed with a hue. For example, pink (red+white) and lavendar(violet+white) are saturated. Degree of saturation is inversely proportional to the amount of white light.

Chromaticity – Hue and saturation together is the chromaticity.

Any color is specified a combination of tristimulus values denotes as X (Red), Y (Green), and Z (Blue)




Characteristics of ColorA color is thus specified by trichromatic coefficients

1

; ;

=++∴++

=++

=++

=

zyxZYX

zzZYX

YyZYX

Xx

CIE chromaticity diagram – For a given value of x (red) and y (green), a corresponding value of z=1-(x+y), i.e., blue is obtainedA color is thus specified by trichromatic coefficients

Any triangle inside chromaticity diagram shows all possible colors which may be obtained mixing only three distinct wavelength primary electromagnetic waves.




Chromaticity Diagram




Color Gamuts

CRT Display

Printing




RGB Color ModelColor space in terms of three primary colors of light, viz. Red,

Green, and Blue

Hardware oriented models, e.g., monitors, video camera, internet etc.

Number of bits used to represent a pixel is called pixel-depth. For a 8-bit image one needs 24 bits in color space

Coordinate24-bit color cube




RGB Color Model

Acquiring the RGB image in the reverse process shown

Three hidden planes in the cube




Safe RGB ColorsIn practice, only 216

colors known as all-system safe colors are used in the internet and represented by two hexadecimal numbers for each color

safe color cube




CMY/CMYK Color ModelColor space in terms of the primary colors of pigment, viz.

Cyan, Magenta, Yellow, or Black

Printer or copier those use pigments on paper considers this model. ‘Four-color printing’ refers to CMYK model

Conversion of RGB and CMY model:

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡−

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

BGR

YMC

111




HSI Color ModelColor space in terms of its Hue, Saturation and

Brightness

The model is used for interpretation of human understanding of colors

This model is used for image processing.

The model decouples the intensity component from the color descriptors

It is known that Hue – purity of color

It is also known that Saturation – degree of pure color (diluted with white color)




HSI and RGB Color ModelIntensity of HSI space is the projection of a point on the

vertical line (connected line between black and white, called intensity axis) shown in the RGB space

Locus of the color points lie on the plane perpendicular to theintensity axis.

Conceptual model in HSI

space




Hue/Saturation in HSI ModelThe color vector perpendicular to the intensity axis creates a

triangular or hexagonal shape with the boundaries of the cube or circular shape inside the cube.

The saturation is the length of the color vector. Hue is the angle of the vector.




Hue/Saturation in HSI Model

Hue and saturation in the triangular or circular color planes




RGB to HSI Model

⎩⎨⎧

>≤

=GBGB

H if360 if

0

θHue

( ) ( )[ ]( ) ( )( )[ ] ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

−−+−

−+−= −

212

21

1coswith BGBRGR

BRGRθ

( ) { }[ ]BGRBGR

S ,,min31++

−=Saturation

( )BGRI ++=31

Intensity




HSI to RGB Model

( ) ( ) ( )BRI-; GH

HSI; RSIB +=⎥⎦

⎤⎢⎣

⎡−

+=−= 360coscos11 0

RG Sector ( )00 1200 <≤ H

GB Sector ( )00 240120 <≤ H

BR Sector ( )00 360240 ≤≤ H

( ) ( ) ( )GRI-; BH

HSI; GSIR +=⎥⎦

⎤⎢⎣

⎡−

+=−= 360coscos11 0

( ) ( ) ( )BRI-; RH

HSI; BSIG +=⎥⎦

⎤⎢⎣

⎡−

+=−= 360coscos11 0

0120−= HH

0240−= HH




(a) (b) (c) (d)

Components of HSI Model

(a) Original RGB image. The component images are (b) Hue (c) Saturation (d) Intensity




Image DemosaicingDemosaicing is a process of obtaining a full color image from

incomplete color samples

A single image sensor associated with the color filter array (CFA) performs the demosaicing

A digital camera may store the raw data so that user-defined software for CFA may be chosen instead of built-in firmware.

Problems of image demosaicing includes chromatic aliases, zippering (abrupt change in intensity), and purple fringing.

Most commonly used CFA for demosaicing is the ‘Bayer Filter’.




Bayer Filter

CFA on pixel array of image sensor

‘Bayer Filter’ has alternating Red (R) and Green (G) filters in odd rows and alternating Blue (B) and Green (G) filters in the even rows.

Green filters are twice since HVS is widely sensitive to Green color!

Optical anti-aliasing filter is used between sensor and lens.

Image due to CFA

Original Reconstructed (Adobe)




Pseudocolor Image Processing

Geometric interpretation

In intensity slicing, a set of intensity levels are coded with a particular color

Functional diagram




Intensity SlicingMonochrome is

color coded

Two colors

Eight colors




Intensity SlicingMonochrome is color coded for SAR images




Pseudo-color ProcessingPseudo-color enhancement by gray level to color transformation

Explosive detection




Pseudo-color ProcessingTwo gray level transformation

functions for detection of explosives in a luggage in a typical airport

Functional diagram




Pseudo-color ProcessingPseudo-color enhancement

of multispectral images

(a) (b) (c) (d) (e) (f)

(a)-(d) Images of four bands. (e) First three are treated as RGB components (f) Infrared image is shown in red




Pseudo-color Processing

Pseudo-color enhancement in terms of material deposition, e.g., sulfur content shown in yellow for Jupiter moon Io




Full-color ProcessingFull-color processing uses independent mask processing of

individual RGB components




Full-color Processing

Components for full color processing in different color spaces




Color TransformationGeneral transformation ( )nii rrrTs ,,, 21 L=

For RGB or CMY or HSI 3=nFor CMYK 4=n

Color mapping

HSI ( ) 221133 10 rsrskkrs ==<<=

RGB ( ) 3,2,1 10 =<<= ikkrs ii

CMY ( ) 3,2,1 10 )1( =<<−+= ikkkrs ii




Color Transformation

Intensity reduction by 33% in three different color spaces using full-color processing




Color Complements

Hues directly opposite to color circle are called color complements

Complements are used to generate color film negatives




Color Complements (a) (b) (c) (d)

(a) Original image (b) Transformation functions for generating complements (c) Resultant image for RGB space (d) Resultant image for HSI space




Color SlicingSelection of a color using a hypercube in a color space

Red color in the RGB space using two different set of cubes




Tone and Color CorrectionsDigital darkroom – Allows tone adjustment and color

corrections digitally by avoiding traditional wet processing

Most common use are photo enhancement and color reproduction in the printing media and compression

For adjustment or corrections device-independent color model should be used

Many color management system (CMS) consider the CIE

or simply CIELAB model

CIELAB is proposed in 1976*** baL




CIELAB ModelThe color components are *** baL

⎥⎦

⎤⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛−⎟⎟

⎠

⎞⎜⎜⎝

⎛=

⎥⎦

⎤⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛−⎟⎟

⎠

⎞⎜⎜⎝

⎛=

−⎟⎟⎠

⎞⎜⎜⎝

⎛⋅=

WW

WW

W

ZZh

YYhb*

YYh

XXha

YYhL

200

500*

16116*

( )⎪⎩

⎪⎨⎧

≤+>

=008856.0116

16787.7008856.03

qqqq

qhwhere




CIELAB ModelThe are the reference white tristimulus

values of a perfectly reflecting diffuser under CIE standard D65illumination, which is defined as x=0.3127 and y=0.3290 in the CIE chromaticity diagram

WWW ZYX ,,

Colormetric – Colors perceived as matching are encoded identically.

Uniform perceptuality – Color differences among various hues are perceived uniformly

Device independent and gamut encompasses the entire visible spectrum

Transformation to another color space is common

Features of color space *** baL




CIELAB Model

Transformation to another color space is necessary

Excellent decoupler of intensity and color for Red-Green and for Green-Blue

Tonal adjustment and color corrections are done independently and interactively (in other words sequentially)

Three common tonal ranges (or key types) are (i) High-key (colors at high intensities) (ii) Low-key (colors at low intensities) (iii) Middle-key (lie in between high and low-keys)

Intensites should be uniform in colors or shadows

Other features of color space *** baL

*L *a*b




Tonal TransformationTonal or contrast

correction and corresponding transformation function in RGB color space

Middle- Key

High- Key

Low- Key




Color TransformationColor balancing

correction and corresponding transformation function in CMYK color space




Histogram ProcessingHistogram equalization

followed by saturation adjustment in HSI color space




Processing of Color Image

RGB Components

HSI Components




Smoothing of Color Image(a) (b) (c)

Image smoothing by 5×5 averaging mask using (a) RGB components and (b) Intensity component of HSI space. (c) Absolute difference between (a) and (b)




Sharpening of Color Image(a) (b) (c)

Image sharpening by Laplacian using (a) RGB components and (b) Intensity component of HSI space. (c) Absolute difference between (a) and (b)




Segmentation of Color ImageApproaches for enclosing data regions for RGB vector

segmentation




Segmentation of Color Image(a) (b)(a) Original RGB image (b)

Segmented region of red color from the image




Gradient of Color ImageGradient of color image

yB

xB

yG

xG

yR

xRg

yB

yG

yRg

xB

xG

xRg

xy

yy

xx

∂∂

∂∂

+∂∂

∂∂

+∂∂

∂∂

=

∂∂

+∂∂

+∂∂

=

∂∂

+∂∂

+∂∂

=

222

222

Maximum rate of change would be at

( )⎥⎥⎦

⎤

⎢⎢⎣

⎡

−= −

yyxx

xy

ggg

yx2

tan21, 1θ

The maximum gradient would be

( ) ( ) ( ) ( ) ( ){ } 21

,2sin2,2cos21, ⎥⎦

⎤⎢⎣⎡ +−++= yxgyxggggyxF xyyyxxyyxx θθθ




Gradient of Color Image

(a) (b) (c) (d)

(a) Original color image (b) Color gradient (c) Added result of individual gradients of RGB components (d) Absolute difference between (b) and (c)

Individual gradients of RGB components




Noisy Color Image

(a) Red (b) Green (c) Blue components of RGB color space for AWGN (d) Noisy color image

(a) (b) (c) (d)




Noisy Color Image

(a) Hue (b) Saturation (c) Intensity components of HSI color image for AWGN

(a) (b) (c)




(a) Color image corrupted by Salt- and-Pepper noise (b) Hue (c) Saturation (d) Intensity components of HSI color space

(a) (b) (c) (d)

Noisy Color Image




AssignmentsProblem #1

Problem #2




AssignmentsProblem #3

Problem #4

Derive the CMY intensity mapping function from its RGB counterpart.

Documents

No Slide TitleBrightness – Achromatic notion of intensity. Hue – Dominant wavelength in the mixture of light waves, i.e., dominant color perceived by observer, say, Red Saturation