10 color image processing
Post on 07-Sep-2014
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Color Image Processing : 1
Color Image ProcessingColor Image Processing
Color Image Processing : 2
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 Image Processing : 3
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
Color Image Processing : 4
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)
Color Image Processing : 5
Light Absorption of ConesLight Absorption of Cones
Visible colors can be visualized as weighted combination of primary colors red, green, and blue
Color Image Processing : 6
Mixtures of Light vs. Mixtures of PigmentsMixtures of Light vs. Mixtures of Pigments
Mixture of light primaries additive
Mixture of pigment primaries subtractive
Color Image Processing : 7
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
Color Image Processing : 8
CIE Chromaticity DiagramCIE Chromaticity Diagram
Color Image Processing : 9
CIE Chromaticity DiagramCIE Chromaticity Diagram
Color Image Processing : 10
CIE Chromaticity Diagram InterpretationCIE Chromaticity Diagram Interpretation
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
Color Image Processing : 11
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!
Color Image Processing : 12
RGB Color Model VisualizationRGB Color Model Visualization
Color Image Processing : 13
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
Color Image Processing : 14
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
Color Image Processing : 15
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)
Color Image Processing : 16
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
Color Image Processing : 17
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
Color Image Processing : 18
Relationship between RGB and HSIRelationship between RGB and HSI
Color Image Processing : 19
HSI Color Model VisualizationHSI Color Model Visualization
Color Image Processing : 20
Converting colors from RGB to HSIConverting colors from RGB to HSI
if
360 if
B GH
B G
1
12 2
12cos
R G R B
R G R B G B
31 [min( , , )]S R G BR G B
1
3I R G B
Color Image Processing : 21
Converting colors from HSI to RGBConverting colors from HSI to RGB
When H is in RG Sector
(1 )B I S
0
cos1
cos(60 )
S HR I
H
3 ( )G I R B
0 0(0 120 )H
Color Image Processing : 22
Converting colors from HSI to RGBConverting colors from HSI to RGB
When H is in GB Sector
(1 )R I S
0
cos1
cos(60 )
S HG I
H
3 ( )B I R G
0 0(120 240 )H
0120H H
Color Image Processing : 23
Converting colors from HSI to RGBConverting colors from HSI to RGB
When H is in RG Sector
(1 )G I S
0
cos1
cos(60 )
S HB I
H
3 ( )R I G B
0 0(240 360 )H
0240H H
Color Image Processing : 24
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
Color Image Processing : 25
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
Color Image Processing : 26
Visualization of Intensity SlicingVisualization of Intensity Slicing
Color Image Processing : 27
Intensity SlicingIntensity Slicing
Color Image Processing : 28
Intensity Slicing ExampleIntensity Slicing Example
Color Image Processing : 29
Intensity Slicing ExampleIntensity Slicing Example
Color Image Processing : 30
Example: Rainfall MonitoringExample: Rainfall Monitoring
Color Image Processing : 31
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
Color Image Processing : 32
Example using Three TransformationsExample using Three Transformations
Color Image Processing : 33
Example: Security ScreeningExample: Security Screening
Color Image Processing : 34
Transformation 1Transformation 1
Garment bag mapped differently than explosive
Easy to spot explosive
Color Image Processing : 35
Transformation 2Transformation 2
Garment bag mapped similar than explosive
Hard to spot explosive
Color Image Processing : 36
Multi-Image PseudocoloringMulti-Image Pseudocoloring
Color Image Processing : 37
Example: Multispectral Image VisualizationExample: Multispectral Image Visualization
Color Image Processing : 38
Point Operations in Color Image ProcessingPoint Operations in Color Image Processing
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
Color Image Processing : 39
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
Color Image Processing : 40
ExampleExample
Color Image Processing : 41
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
Color Image Processing : 42
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
Color Image Processing : 43
Tone Correction for Common Tonal ImbalancesTone Correction for Common Tonal Imbalances
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)
Color Image Processing : 44
Example Tonal CorrectionsExample Tonal Corrections
Color Image Processing : 45
Point Operations for Color CorrectionPoint Operations for Color Correction
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 Image Processing : 46
Color CorrectionsColor Corrections
Color Image Processing : 47
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 Image Processing : 48
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.
Color Image Processing : 49
Types of dichromatic color vision deficienciesTypes of dichromatic color vision deficiencies
Protanopia: L cones are absent or defective
Deuteranopia: M cones are absent or defective
Tritanopia: S cones are absent or defective (rare)
Color Image Processing : 50
Types of dichromatic color vision deficienciesTypes of dichromatic color vision deficiencies
Protanopia and deuteranopia are often referred to as red-green color blindness.
Tritanopia is often referred to as blue-yellow color blindness.
Color Image Processing : 51
So what may dichromats see?So what may dichromats see?
Color Image Processing : 52
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
Color Image Processing : 53
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
Color Image Processing : 54
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 Image Processing : 55
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.
Color Image Processing : 56
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
Color Image Processing : 57
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.
Color Image Processing : 58
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.
Color Image Processing : 59
Non-linear Hue RemappingNon-linear Hue Remapping
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 h
Color Image Processing : 60
Non-linear Hue RemappingNon-linear Hue Remapping
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.
Color Image Processing : 61
Adaptive Hue RemappingAdaptive Hue Remapping
The parameter Φ controls the curvature of the power function.
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 Φ will compress the blue hue range and stretch the other hue ranges without any perceptual benefit.
As such, it is necessary to adaptively adjust the value of Φ based on the underlying image content to achieve enhanced perceptual quality.
Color Image Processing : 62
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
Color Image Processing : 63
Examples of Color CorrectionExamples of Color Correction
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