digital image processing color processing

The field of digital image processing refers to processing digital images by means of digital
computer. Digital image is composed of a finite number of elements, each of which has a particular
location and value. These elements are called picture elements, image elements, pels and pixels.
Pixel is the term used most widely to denote the elements of digital image.
An image is a two-dimensional function that represents a measure of some characteristic
such as brightness or color of a viewed scene. An image is a projection of a 3-D scene into a 2D
projection plane.
Since digital image processing has very wide applications and almost all of the technical fields
are impacted by DIP, we will just discuss some of the major applications of DIP.
Digital image processing has a broad spectrum of applications, such as
 Remote sensing via satellites and other spacecrafts
 Image transmission and storage for business applications
 Medical processing,
 RADAR (Radio Detection and Ranging)
 SONAR(Sound Navigation and Ranging)
 Acoustic image processing (The study of underwater sound is known as underwater
acoustics or hydro acoustics.)
 Robotics and automated inspection of industrial parts.IMAGE PROCESSING TOOLBOX (IPT) is a collection of functions that extend the
capability of the MATLAB numeric computing environment. These functions, and the
expressiveness of the MATLAB language, make many image-processing operations easy to write
in a compact, clear manner, thus providing a ideal software prototyping environment for the
solution of image processing problem.Specialize image processing hardware: It consists of the digitizer just mentioned, plus hardware that
performs other primitive operations such as an arithmetic logic unit, which performs arithmetic such
addition and subtraction and logical operations in parallel on images.
Computer: It is a general purpose computer and can range from a PC to a supercomputer depending
on the application. In dedicated applications, sometimes specially designed computer are used to
achieve a required level of performance
Software: It consists of specialized modules that perform specific tasks a well designed package also
includes capability for the user to write code, as a minimum, utilizes the specialized module. More
sophisticated software packages allow the integration of these modules.
Mass storage: This capability is a must in image processing applications. An image of size 1024 x1024
pixels, in which the intensity of each pixel is an 8- bit quantity requires one Megabytes of storage space
if the image is not compressed .Image processing applications falls into three principal categories of
storageImage acquisition: It could be as simple as being given an image that is already in digital form.
Generally the image acquisition stage involves processing such scaling.
Image Enhancement: It is among the simplest and most appealing areas of digital image
processing. Image Restoration: Color Wavelets a

1. Digital Image Processing
DAY 3- 24/6/2020







TODAYS TOPICS:
• Colour image fundamentals
• RGB
• HSI models

2. Main types of digital
image
Colour image (RGB, CMYK)
•An RGB image has 24-bit information (millions of
colours)
•Channels Red, Green and Blue have 256 colours
each.
•For screen images.
•A CMYK image has a 32-bit information
•Channels Cyan, Magenta, Yellow, and Black have
256 colours each.
•For images used in printing offices.
Indexed colour
•8-bit image information. Availability of 256 colours.
•Suits well for coloured drawing graphics, but not
for photographs.
•For screen images.

3. Main types of digital image CONT…..
Line-art, bitmap
•An image element consists of 1 bit; 1 = white
and 0 = black.
•Suitable for the presentation of drawings, for
instance, drawing ink works, black and white line
graphics, and texts.
•Requires little saving space due to the lack of
colours, but requires high resolution in order to
show details accurately.
Grayscale image
•The image has 8 bits and 256 tones of grey;
• 1 = black and 255 = white.
•Requires 8 times more saving space than a line-
art image.
•Suitable for presenting black and white
photographs, for instance.
Can be used in printing office.

4. Colorimetric
Color Perception involve Hue, Saturation, and Lightness
Hue :Distinguish among colors such as red, green, and
purple.
Saturation :Refer to how color far from gray.white light
mixed with Hue

Lightness: The perceived intensity of a reflecting object.
Brightness :Refer to the perceived intensity of self-luminous.
White “Pure”
color
Grays
Shades
Tones
Black
© 1992–2008 R.C. Gonzalez & R. E. Woods
Artists Terms
Tint: results of adding white pigments
pure pigments
Shade: comes from adding black pigments
to pure pigments
Tone:results of adding both black and white
pigments to pure pigments

5. Color Image Processing
Green
Color Models:
The Newton Color Circle
Cyan Yellow
Blue Red
Magenta

•The Newton color circle provides a convenient way to perceive
the additive mixing properties of colors.
•The R,G,B and their complementary colors C,M,Y are placed on
the circle in the order of the wavelengths of the corresponding
spectral colors.

6. Digital Image Processing, 3rd ed.
Gonzalez & Woods
www.ImageProcessingPlace.com
Chapter 6
Color Image Processing
•The separation of colors by a prism expose a continuous range
of spectral colors
© 1992–2008 R. C. Gonzalez & R. E. Woods

7. Digital Image Processing, 3rd ed.
Gonzalez & Woods
www.ImageProcessingPlace.com
Chapter 6
Color Image Processing
•A spectral color is composed of a single wavelength
•The helium-neon laser monochromatic light is red (632 nm).
•Most colored objects give off a range of wavelengths and the
characterization of color is much more than the statement of
wavelength.
© 1992–2008 R. C. Gonzalez & R. E. Woods

8. Digital Image Processing, 3rd ed.
Gonzalez & Woods
www.ImageProcessingPlace.com
Chapter 6
Color Image Processing
© 1992–2008 R. C. Gonzalez & R. E. Woods

9. Primary and Secondary colors

10. CIE Chromatic Diagram
Chromatic values depend on dominant
wavelength and saturation and independent
of luminous energy.
Consider a color C, the we can Write C =
XX + YY + ZZ
Normalize Against X+Y+Z
The XYZ space
x = X/(X+Y+Z) y = Y/(X+Y+Z) z =
Z/(X+Y+Z)
We know that x+y+z = 1, and the
luminance information usually in Y (Y
cef.), thus we can recover X,Y,Z
X = Y (x/y) ; Y = Y ; Z = Y(1-x-y)/y
Plotting these parameters
© 1992–2008 R. C. Gonzalez & R. E. Woods

11. XYZ Space

13. cessing

When two color A and B are added together new color C
lies on the line connects both colors.
In the side Figure, B defines the dominant wavelength,
and the ratio AC to BC expressed as a percent of the
excitation purity of A.
The closer A to C the more light A includes.

Complementary colors are those that can be mixed to
produce white light. D and E on the side Figure are
complementary colors.
Nonspectral color are those that can not be defined by
dominant wavelength such as F. Color gamuts or color
ranges is the effect of adding colors together
© 1992–2008 R. C. Gonzalez & R. E. Woods

14. .
Color Image Processing
© 1992–2008 R. C. Gonzalez & R. E. Woods

16. RGB Color Model
The color range (gamut) RGB
model is defined by the CRT’s
phosphor.

C = RR + GG + BB

Let us look at these colors in
XYZ space RGB NTSC
CIE Monitor
R (0.67, 0.33)
G (0.21, 0.71)
(0.73, 0.26) (0.62, 0.34)
(0.27, 0.71) (0.26, 0.59)
B (0.14, 0.08) (0.16, 0.01) (0.15, 0.07)
© 1992–2008 R. C. Gonzalez & R. E. Woods

17. Generating Image of Three Color Cross sectional Colour Plane (127,G,B)

18. Digital Image Processing, 3rd ed.
Gonzalez & Woods
www.ImageProcessingPlace.com
Chapter 6
Color Image Processing
© 1992–2008 R. C. Gonzalez & R. E. Woods

19. CMY Color Model

Cyan, magenta, and blue are the complements of red, green, blue.
CMY is important when dealing with hardcopy that deposit color
pigments onto paper.

 C  1  R

M



1



G

     

 Y 


1


B

 R 1  C 
1
    

 B


1


 Y 


G



1G



M

© 1992–2008 R. C. Gonzalez & R. E. Woods

21. In various color processing
applications it is possible to
use the corresponding Red,
Green, and Blue channels
© 1992–2008 R. C. Gonzalez & R. E. Woods

23. .

24. Color Coding for
visualization
© 1992–2008 R. C. Gonzalez & R. E. Woods

25. Digital Image Processing

Color Image Processing

26. Colour Image Processing

27. Digital Image Processing.
Color Image Processing
Color Transformation
© 1992–2008 R. C. Gonzalez & R. E. Woods

28. Digital Image Processing, 3rd ed.
Color Image Processing
© 1992–2008 R. C. Gonzalez & R. E. Woods

29. Digital Image Processing
Color Image Processing
© 1992–2008 R. C. Gonzalez & R. E. Woods

30. Digital Image Processing
Color Image Processing
© 1992–2008 R. C. Gonzalez & R. E. Woods

31. Digital Image Processing, 3rd ed.
Color Image Processing
© 1992–2008 R. C. Gonzalez & R. E. Woods

33. Digital Image Processing, 3rd ed.
Color Image Processing
© 1992–2008 R. C. Gonzalez & R. E. Woods

34. Digital Image Processing, 3rd ed.
Color Image Processing
© 1992–2008 R. C. Gonzalez & R. E. Woods

35. © 1992–2008 R. C. Gonzalez & R. E. Woods

36. Digital Image Processing
Color Image Processing
© 1992–2008 R. C. Gonzalez & R. E. Woods

37. Color Image Processing
© 1992–2008 R. C. Gonzalez & R. E. Woods

38. Color Image Processing

39. © 1992–2008 R. C. Gonzalez & R. E. Woods

43. © 1992–2008 R. C. Gonzalez & R. E. Woods

44. © 1992–2008 R. C. Gonzalez & R. E. Woods

45. Color Image Processing