chapter 6: color image processing (6.1...

Digital Image ProcessingChapter 6: Color Image Processing

(6.1 – 6.3)

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6. Preview

• The process followed by the human brain in perceiving and interpreting color is a physiopsychological henomenon that is not yet fully understood, the physical nature of color can be expressed on a formal basis supported by experiment and theoretical results.

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6.1 Color Fundamentals

• The process followed by the human brain in perceiving and interpreting color is a physiopsychological henomenon that is not yet fully understood, the physical nature of color can be expressed on a formal basis supported by experiment and theoretical results.

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6. Color Image Processing

• In 1666, Sir Isaac Newton discovered that when a beam of sunlight passes through a glass prism, the emerging beam of light us not white but consist in stead of a continuous spectrum of colors ranging from violet at one end to red at the other.• six broad regions : violet, blue, green, yellow, orange, and red

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Visible light is composed of a relatively narrow band of frequencies in the electromagnetic spectrum

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• average experimental curves detailing the absorption of light by the reg, green, blue cones in the eye

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• Three basic quantities are used to describe the quality of a chromatic light source: radiance, luminance, and brightness.– Radiance is the total amount of energy that flow from the light source, and it is usually measured in watts (W).– Luminance, measured in lumens (lm), gives a measure of the amount of energy an observer perceives from a light source.– Brightness is a subjective descriptor that is practically impossible to measure. It embodies the achromatic notion of intensity and is one of the key factors in describing color sensation.

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• three primary colors and colors of pigments and their combinations to produce the second colors

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6.1 Color Fundamentals

• The characteristics generally used to distinguish one color from another are brightness, hue, and saturation.-Hue : attribute associated with the dominant wavelength in a mixture of light waves.-saturation : relative puriry or the amount of white light mixed with a hue.• The amounts of red, green , blue needed to form any particular color are called the tristimuus values and are denoted X, Y, Z

𝒙 =𝑿

𝑿 + 𝒀 + 𝒁𝒚 =

𝒀

𝑿 + 𝒀 + 𝒁𝒛 =

𝒁

𝑿 + 𝒀 + 𝒁

𝒙 + 𝒚 + 𝒛 = 𝟏

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• another approch for specifying colors is to use the CIE chromaticity diagram, which shows color compositin as a function of x(red) and y(green), z(blue).

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• the triangle shows a typical range of colors produced by RGB monitors.

6.2.1 The RGB Color Model

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Gray Scale

(0, 0, 0)

(1, 1, 1)

RGB primary values(Red, Green, Blue)

Secondary colors(Cyan, Magenta, Yellow)

All of RGB range : [0, 1]

Each of RGB have a 8-bit Pixel depth

∴RGB triplet have a 24-bit Pixel depth

Total number of colors

(28)3= 16,777,216

(It often denote Full Color Image)


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Three individual component images(Gray scale images)

Color Moniter

One of surface plane of Fig 6.8


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Safe RGB Colors(Safe Web Colors or

All Systems Safe Colors)

It means subset of colors that

Variety of systems

reproduced faithfully

independently of hardware

Each of RGB values can only

0, 51, 102, 153, 204, 255 (Tab 6.1)

(RGB triplet have 63 = 216)

6.2.2 The CMY and CMYK Color Model

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Cyan, Magenta, and Yellow

- Secondary Colors of Light

- Primary Values of Pigments

(It absorb opposite color)

RGB to CMY Transform

𝑪

𝑴

𝒀

=

𝟏

𝟏

𝟏

-

𝑹

𝑮

𝑩

(All of colors range : [0, 1])

- Ex) Pure Megenta not reflect Green

CMYK Color

- C + M + K = muddy looking Black

(so add a fourth color, Black)

6.2.3 The HSI Color Model

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HSI(Hue, Saturation, intensity)Hue : 색상Saturation : 채도Intensity : 명도 / 밝기

HSI Color Model decouples the intensity component from the color-carrying information(hue and saturation) in a color image.

The HSI color model is an ideal tool for developing image processing algorithms based on color descriptions that are natural and intuitive to humans.


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(6.2-2)

GB

GBH

360

2/12

21

1

)])(()[(

)]()[(cos

BGBRGR

BRGR

)],,[min()(

31 BGR

BGRS

)(3

1BGRI

Converting colors from RGB to HSI

(6.2-3)

(6.2-4)


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(6.2-5))1( SIB

])60cos(

cos1[

H

HSIR

)(3 BRIG

Converting colors from HSI to RGB

(6.2-7)

(6.2-6)

RG Sector )1200( H


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(6.2-9))1( SIR

])60cos(

cos1[

H

HSIG

)(3 GRIB


(6.2-10)

GB Sector )240120( H

(6.2-8)120 HH

(6.2-11)


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)1( SIG

])60cos(

cos1[

H

HSIB

)(3 GRIR


(6.2-12)

BR Sector )360240( H

240 HH

(6.2-15)

(6.2-13)

(6.2-14)


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Example 6.2 The HSI values corresponding to the image of the RGB color cube


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Manipulating HSI component images


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Manipulating HSI component images

6.3 Pseudocolor Image Processing

• The pseudocolors used to colorize a gray level image do not represent the original, true colors if there were originally a color image.

• The principal use of pseudocolor is for human visualization and interpretation of gray-scale events in an image or sequence of images.

6.3.1 Intensity Slicing

• Intensity slicing and colour coding is one of the simplest kinds of pseudocolour image processing

Let [0, L-1] represent the grey scale

Let l0 represent black [f(x, y) = 0] and let lL-1 represent white [f(x, y) = L-1]

Suppose P planes perpendicular to the intensity axis are defined at levels l1, l2, …, lpAssuming that 0 < P < L-1 , the P planes partition the gray scale into P + 1 intervals V1, V2,…,VP+1

(6.3 - 1)

where Ck is the colour associated with the kth intensity level Vk defined by the partitioning planes at l = k – 1 and l = k

f (x,y) ck if f (x,y)Vk

6.3.1 Intensity SlicingA gray scale image viewed as a 3D surface.

FIGURE 6.18 Geometric interpretation of the intensity-slicing technique.


FIGURE 6.19 An alternative representation of the intensity-slicing technique.


An X-ray image of the PickerThyroid Phantom.

After density slicing into 8 colors

FIGURE 6.20 (a) Monochrome image of the Picker Thyroid Phantom. (b) Result of density slicing into eight colors. (Courtesy of Dr. J. L. Blankenship, Instrumentation and Controls Division, Oak Ridge National Laboratory.)

(a) (b)


An X-ray image of a weld with cracks

After assigning a yellow color to pixels with

value 255 and a blue color

to all other pixels.

FIGURE 6.21 (a) Monochrome X-ray image of a weld. (b) Result of color coding.(Original image courtesy of X-TEK Systems, Ltd.)

(a) (b)


FIGURE 6.22

(a) Gray-scale image in which intensity (in the lighter horizontal band shown) corresponds to average monthly rainfall.

(b) Colors assigned to intensity values.

(c) Color-coded image. (d) Zoom of the South A

merica region.

(Courtesy of NASA.)

(b)

(c) (d)

(a)

Gray-scale image

Color-coded image

6.3.2 Intensity Color Transformations

• Other types of transformations are more general and thus are capable of achieving a wider range of pseudocolorenhancement results than the simple slicing technique discussed in the preceding section.


Assigning colors to gray levels based on specific mapping functions

FIGURE 6.23 Functional block diagram for pseudocolor image processing. fR, fG, and fB are fed into the corresponding red, green, and blue inputs of an RGB color monitor.


FIGURE 6.24 Pseudocolor enhancement by using the gray-level to color transformations in Fig. 6. 25.

(Original courtesy of Dr. Mike Hurwitz, Westinghouse.)

(a)

(b) (c)

An X-ray image of a garment bag

Color

codedimages

An X-ray image of a

garment bag with a

simulated explosivedevice


(b) (c)

FIGURE 6.25Transformation functions used to obtain the image

in Fig.6.24..


FIGURE 6.26 A pseudocolor coding approach used when several monochrome image are available.

(b) (c)

Used in the case where there are many monochrome images such as multispectral satellite images.

Ex.6.6 Color coding of multispectral images

The first three images are in the visible red, green and blue and the fourth is in the near infrared(see Table 1.1 and Fig. 1.10). Figure 6.27(e) is the full-color image obtained by combining the first three images into an RGB image. Full-color images of dense areas are difficult to interpret, but one notable feature of this image is the difference in color in various parts of the Potomac River. Figure 6.27(f) This image was formed by replacing the red component of Fig. 6.27(e) with the near-infrared image.

Ex. 6.6 Color coding of multispectral images

These are images of the Jupiter moon Io, shown in pseudocolor by combining several of the sensor images from the Galileospacecraft, some of which are in spectral regions not visible to the eye. However, by understanding the physical and chemical processes likely to affect sensor response, it is possible to combine the sensed images into a meaningful pseudocolr map. One way to combine the sensed image data is by how they show either differences in surface chemical composition or changes in the way the surface reflects sunlight.

chapter 6: color image processing (6.1...

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