10 color image processing

Color Image Processing : 1
Color Image ProcessingColor Image Processing

Visible LightVisible Light
Visible light composed of relatively narrow band of frequencies in
electromagnetic spectrum
Chromatic light spans EM spectrum from around 400 to 700 nm

Color PerceptionColor Perception
Perceived color of an object based on nature of light reflected from
object
Examples:
If object reflects light that's balanced from all visible
wavelengths, object is perceived as white
If object reflects light with wavelengths mainly in the 575 to
625nm range, object is perceived as red

Cones RevisitedCones Revisited
6 to 7 million cones in the human eye
Divided into three main types:
L cones (65%)
 Maximally sensitive to long wavelengths (e.g., red)
M cones (33%)
 Maximally sensitive to medium wavelengths (e.g., green)
S cones (2%)
 Maximally sensitive to short wavelengths (e.g., blue)

Light Absorption of ConesLight Absorption of Cones
Visible colors can be visualized as weighted combination of primary colors
red, green, and blue

Mixtures of Light vs. Mixtures of PigmentsMixtures of Light vs. Mixtures of Pigments
Mixture of light primaries additive
Mixture of pigment primaries subtractive

CIE Chromaticity DiagramCIE Chromaticity Diagram
A method for specifying colors
Specifies color composition as function of x (red) and y (green)
For any value of x and y, value of z (blue) can be found as
The (x,y,z) values of a color specifies percentage of red, green, and
blue needed to form the color (Trichromatic Coefficients)
1 ( )z x y= − +

InterpretationInterpretation
Pure spectrum colors located around boundary
All non-boundary colors are mixture of spectrum colors
Point of equal energy corresponds to equal fractions of the three
primary colors
CIE standard for white light
Straight line segment joining two points define all colors that can be
created by mixing these two colors additively

RGB Color ModelRGB Color Model
Primarily used for displays and cameras
Based on Cartesian coordinate system
Three axis represents intensities of red, green, and blue
Gray scale (points of equal RGB values) extends from black (0,0,0)
to white (1,1,1)
Example: 24-bit color (Truecolor)
8-bits (256 levels) are used to represent each channel
Gives a total of (256)3
=16,777,216 possible colors!

RGB Color Model VisualizationRGB Color Model Visualization

CMY/CMYK Color ModelsCMY/CMYK Color Models
Primarily used for printing
Based on primary colors of pigments
For CMY, the three axis represent the amount of cyan, magenta,
and yellow pigments to put in to produce a certain color
1
1
1
C R
M G
Y B
     
     = −     
          

Why K?Why K?
In theory, equal amounts of cyan, magenta, and yellow produces
black
In practice, combining them results in muddy-looking black
To produce true black in printing, a fourth color (black) is added to
produce the CYMK color model

Pros and Cons of RGBPros and Cons of RGB
Advantages of RGB model:
Straightforward (great for hardware implementation)
Matches well with human vision system's strong response to
red, green, and blue
Disadvantage of RGB model:
Difficult for human description of color (e.g., humans don't
describe color as RGB percentages)
Highly redundant and correlated (e.g., all channels hold
luminance information, reduces coding efficiency)

HSI Color ModelHSI Color Model
Useful for human color interpretation
Three axis represent:
Hue
 Describes pure/dominate color perceived by observer (e.g.,
pure yellow, orange, red)
Saturation (Purity of color)
 Amount of white light mixed with hue
 High saturation = high purity = little white light mixed with
hue
Intensity
 Brightness

Relationship between RGB and HSIRelationship between RGB and HSI
Hue: all colors on plane defined by white, black, and a pure color corner
point have same hue
Saturation: distance from associated pure color
Intensity: projection to gray scale line

Relationship between RGB and HSIRelationship between RGB and HSI

HSI Color Model VisualizationHSI Color Model Visualization

Converting colors from RGB to HSIConverting colors from RGB to HSI
if
360 if
B G
H
B G
θ
θ
≤
= 
− >
( ) ( )
( ) ( ) ( )
1
1
2 2
1
2cos
R G R B
R G R B G B
θ −
 
− + −   
=  
  − + − −
  
3
1 [min( , , )]S R G B
R G B
= −
+ +
[ ]
1
3
I R G B= + +

Converting colors from HSI to RGBConverting colors from HSI to RGB
When H is in RG Sector
(1 )B I S= −
0
cos
1
cos(60 )
S H
R I
H
 
= + − 
3 ( )G I R B= − +
0 0
(0 120 )H≤ ≤

When H is in GB Sector
(1 )R I S= −
0
cos
1
cos(60 )
S H
G I
H
 
= + − 
3 ( )B I R G= − +
0 0
(120 240 )H≤ ≤
0
120H H= −

When H is in RG Sector
(1 )G I S= −
0
cos
1
cos(60 )
S H
B I
H
 
= + − 
3 ( )R I G B= − +
0 0
(240 360 )H≤ ≤
0
240H H= −

Pseudocolor Image ProcessingPseudocolor Image Processing
Goal
Assign color to gray levels to convert grayscale image into color
image
Why?
Improve visualization of image information
Motivation
Humans can discern thousands of color shades but only two
dozen or so gray shades

Intensity SlicingIntensity Slicing
One of the simplest methods for pseudocolor image processing
Grayscale image can be viewed as 3D function (x,y, and intensity)
Suppose we define P planes perpendicular to intensity axis
Each plane i is associated with a color Ci
Pixels with intensities lying along a particular plane i is assigned the
color Ci corresponding to the plane

Visualization of Intensity SlicingVisualization of Intensity Slicing

Intensity SlicingIntensity Slicing

Intensity Slicing ExampleIntensity Slicing Example

Example: Rainfall MonitoringExample: Rainfall Monitoring

Gray Level to Color TransformationsGray Level to Color Transformations
Intensity slicing limits range of pseudocolor enhancement results
Fixed one-to-one relationship between intensity and specified
colors
Alternative solution:
Process grayscale image using independent transformations
The results of the transformations are combined to create one
composite color image

Example using ThreeExample using Three
TransformationsTransformations

Example: Security ScreeningExample: Security Screening

Transformation 1Transformation 1
Garment bag mapped differently
than explosive
Easy to spot explosive

Transformation 2Transformation 2
Garment bag mapped similar than
explosive
Hard to spot explosive

Multi-Image PseudocoloringMulti-Image Pseudocoloring

Example: Multispectral ImageExample: Multispectral Image
VisualizationVisualization

Point Operations in Color ImagePoint Operations in Color Image
ProcessingProcessing
Similar to point processing for grayscale images
Example: RGB color model
n = 3
r1,r2,r3 denotes red, green, blue components of the input
image
1 2( , ,..., ), 1,2,...,i i ns T r r r i n= =

What are Color Complements?What are Color Complements?
Hues opposite one another on the color circle
Analogous to grayscale inverses
Useful for enhancing details in dark regions of image

ExampleExample

Point Operations for Tone CorrectionPoint Operations for Tone Correction
Tonal range: general distribution of color intensities
Similar to intensity contrast in grayscale images
High-key images
Colors concentrated at high intensities
Low-key images
Colors concentrated at low intensities
As with grayscale images, it is desirable to distribute color
intensities evenly

Point Operations for Tone CorrectionPoint Operations for Tone Correction
Before correcting color imbalances, tonal imbalances are first
corrected
All color channels are transformed using the same transformation
for color models where intensity information is spread across
multiple channels (e.g., RGB, CMY)
For HSI color model, only I channel is modified
Operations are similar to intensity contrast adjustment for grayscale
images

Tone Correction for Common TonalTone Correction for Common Tonal
ImbalancesImbalances
Flat images
Use an s-curve transformation to boost contrast
 lighten highlight areas
 darken shadow areas
Light and dark images
Similar to power-law transformations
Stretch light regions and compress dark regions for light
images (high gamma)
Stretch dark regions and compress light regions for dark
images (low gamma)

Example Tonal CorrectionsExample Tonal Corrections

Point Operations for ColorPoint Operations for Color
CorrectionCorrection
Various ways to correct color imbalances
Perception of a color affected by surrounding colors
Proportion of any color (e.g., magenta) can be reduced by
Increasing its complementary color (e.g., green)
Decreasing portion of the two immediately adjacent colors (e.g.,
red and blue)

Color CorrectionsColor Corrections

Histogram EqualizationHistogram Equalization
Histogram equalization on
individual color channels
leads to erroneous colors
Better approach is to just
equalize intensity
component and leave colors
(i.e., hues) unchanged

Color vision deficienciesColor vision deficiencies
Statistics show that color vision deficiencies affect 8.7% of the male
population and 0.4% of the female population.
Dichromacy is a form of color vision deficiency that severely affects
an individual’s ability to differentiate hues.
Dichromacy has no known cure.

Types of dichromatic color visionTypes of dichromatic color vision
deficienciesdeficiencies
Protanopia: L cones are absent or defective
Deuteranopia: M cones are absent or defective
Tritanopia: S cones are absent or defective (rare)

Types of dichromatic color visionTypes of dichromatic color vision
deficienciesdeficiencies
Protanopia and deuteranopia are often referred to as red-green
color blindness.
Tritanopia is often referred to as blue-yellow color blindness.

So what may dichromats see?So what may dichromats see?

Color Correction ApproachesColor Correction Approaches
There are two main approaches to color correction for helping
individuals cope with the medical condition:
Fixed color correction
Adaptive color correction

Fixed Color Correction ApproachFixed Color Correction Approach
Perform a fixed color transformation on the image
Improves color differentiation to make details more visible
Problem: The aesthetics of the original scene is poorly captured

Adaptive Color Correction ApproachAdaptive Color Correction Approach
Solution: Adapt color transformation based on the underlying hue
characteristics of the image
Advantages:
Improves color differentiation
Preserves aesthetic appeal of the original scene

Color space transformationColor space transformation
The main difficulty encountered by those suffering from dichromacy
is the inability to differentiate between certain hues.
An effective approach to color enhancement is to alter the hue
distribution of an image in such a way that hue discrimination is
improved and details within an image become more perceivable by
those suffering from dichromacy.
To preserve the aesthetic properties of the original image, it is also
desired that other characteristics of the image such as illumination
and saturation are left unchanged.
To accomplish this goal, the image is converted from the RGB
color-space to the HSI color-space.

Hue RemappingHue Remapping
A simple method of improving hue discrimination within the
indistinguishable hue range is to perform a circular hue shift such
that hues that can be easily discriminated are used to represent
this hue range.
One of the major disadvantages of this technique is that such a
uniform hue shift results in highly unnatural images.
The reason for this is that most of the hues that are actually
correctly recognized by those suffering from dichromacy are now
misrepresented by the hue shift.
What if we do a hue compression instead?
Allows some of the hues that can be correctly recognized to
be assigned much of the same hue as before

Hue RemappingHue Remapping
Improves hue discrimination and maintains some of the
distinguishable hues
Problem: the uniform nature of such transforms result in significant
loss of dynamic range in the distinguishable portions as well as
unnatural color re-mappings in many areas given the fixed
redistribution.

Non-linear Hue RemappingNon-linear Hue Remapping
First, rotate hue space such that the two hues that were
indistinguishable are at the front of the spectrum while the third hue
is at the end.
For example, in the case of protanopia and deuteranopia, the hue
range containing the red and green hue components are rotated to
the front of the spectrum while the blue hue range is at the end.

A hue remapping can then be performed on the rotated hue space
in the form of a power transformation function:
This hue remapping does two things:
The range of hues that are indistinguishable
(e.g., red-yellow-green range) are stretched over a wider
dynamic range, thereby increasing the hue discrimination for
that range of hues.
The range of the hue that is distinguishable from the rest of the
spectrum (e.g. blue) is compressed, thereby having part of its
dynamic range being redistributed to the indistinguishable
range.
( )f h hφ
=

By using a nonlinear remapping function, the range re-distribution is
varied over the spectrum and therefore allows for greater flexibility
in maintaining the aesthetic feel of the original image.
After the hue remapping, the hue space is rotated back to its
original position.

Adaptive Hue RemappingAdaptive Hue Remapping
The parameter controls the curvature of the power function.Φ
A simple approach is to set the control parameter at a fixed value.
The main problem to this approach is that hue distribution varies
greatly from one image to another.
For example, an image may consists of only blue hues. Therefore,
a fixed value of will compress the blue hue range and stretch theΦ
other hue ranges without any perceptual benefit.
As such, it is necessary to adaptively adjust the value of basedΦ
on the underlying image content to achieve enhanced perceptual
quality.

Adaptive Hue RemappingAdaptive Hue Remapping
If the hue distribution resides mostly in the indistinguishable range,
then the control parameter should be increased to stretch this
range to improve hue discrimination and attenuate image details.
However, if the hue distribution resides mostly outside this range,
then the control parameter should be decreased to preserve the
original hue distribution.
This can be determined based on histogram

Examples of Color CorrectionExamples of Color Correction

10 color image processing

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10 color image processing