1. There are two types of color image processing: pseudocolor processing which assigns colors to grayscale images, and full color processing which manipulates real color images.
2. The human visual system perceives color through photoreceptor cells (cones) in the retina that are sensitive to red, green, and blue wavelengths. Color images can be represented in various color spaces like RGB, HSI, CMYK.
3. Pseudocolor processing techniques include intensity slicing, color coding, and gray level to color transformations to visualize grayscale images. Full color processing involves operations on color components like color balancing, complement, slicing, smoothing and sharpening.
COM2304: Intensity Transformation and Spatial Filtering – I (Intensity Transf...Hemantha Kulathilake
At the end of this lesson, you should be able to;
describe spatial domain of the digital image.
recognize the image enhancement techniques.
describe and apply the concept of intensity transformation.
express histograms and histogram processing.
describe image noise.
characterize the types of Noise.
describe concept of image restoration.
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
color image processing is divided into two major areas:
1. Full Color image Processing
2. Pseudo Color image Processing
It Includes Color Fundamentals,Color Models,Pseudo color image Processing,Full Color image Processing,Color Transformation.
Lecture 1 for Digital Image Processing (2nd Edition)Moe Moe Myint
-What is Digital Image Processing?
-The Origins of Digital Image Processing
-Examples of Fields that Use Digital Image Processing
-Fundamentals Steps in Digital Image Processing
-Components of an Image Processing System
Fundamental concepts and basic techniques of digital image processing. Algorithms and recent research in image transformation, enhancement, restoration, encoding and description. Fundamentals and basic techniques of pattern recognition.
In color image processing, an abstract mathematical model known as color space is used to characterize the colors in terms of intensity values. This color space uses a three-dimensional coordinate system. For different types of applications, a number of different color spaces exists.he saturation is determined by the excitation purity, and depends on the amount of white light mixed with the hue. A pure hue is fully saturated, i.e. no white light mixed in. Hue and saturation together determine the chromaticity for a given colour. Finally, the intensity is determined by the actual amount of light, with more light corresponding to more intense colours[1].
Achromatic light has no colour - its only attribute is quantity or intensity. Greylevel is a measure of intensity. The intensity is determined by the energy, and is therefore a physical quantity. On the other hand, brightness or luminance is determined by the perception of the colour, and is therefore psychological. Given equally intense blue and green, the blue is perceived as much darker than the green. Note also that our perception of intensity is nonlinear, with changes of normalised intensity from 0.1 to 0.11 and from 0.5 to 0.55 being perceived as equal changes in brightnes.Colour depends primarily on the reflectance properties of an object. We see those rays that are reflected, while others are absorbed. However, we also must consider the colour of the light source, and the nature of human visual system. For example, an object that reflects both red and green will appear green when there is green but no red light illuminating it, and conversely it will appear red in the absense of green light. In pure white light, it will appear yellow (= red + green).The pure colours of the spectrum lie on the curved part of the boundary, and a standard white light has colour defined to be near (but not at) the point of equal energy x = y = z = 1/3. Complementary colours, i.e. colours that add to give white, lie on the endpoints of a line through this point. As illustrated in figure 4, all the colours along any line in the chromaticity diagram may be obtained by mixing the colours on the end points of the line. Furthermore, all colours within a triangle may be formed by mixing the colours at the vertices. This property illustrates graphically the fact that all visible colours cannot be obtained by a mix of R, G and B (or any other three visible) primaries alone, since the diagram is not triangular!
COM2304: Intensity Transformation and Spatial Filtering – I (Intensity Transf...Hemantha Kulathilake
At the end of this lesson, you should be able to;
describe spatial domain of the digital image.
recognize the image enhancement techniques.
describe and apply the concept of intensity transformation.
express histograms and histogram processing.
describe image noise.
characterize the types of Noise.
describe concept of image restoration.
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
color image processing is divided into two major areas:
1. Full Color image Processing
2. Pseudo Color image Processing
It Includes Color Fundamentals,Color Models,Pseudo color image Processing,Full Color image Processing,Color Transformation.
Lecture 1 for Digital Image Processing (2nd Edition)Moe Moe Myint
-What is Digital Image Processing?
-The Origins of Digital Image Processing
-Examples of Fields that Use Digital Image Processing
-Fundamentals Steps in Digital Image Processing
-Components of an Image Processing System
Fundamental concepts and basic techniques of digital image processing. Algorithms and recent research in image transformation, enhancement, restoration, encoding and description. Fundamentals and basic techniques of pattern recognition.
In color image processing, an abstract mathematical model known as color space is used to characterize the colors in terms of intensity values. This color space uses a three-dimensional coordinate system. For different types of applications, a number of different color spaces exists.he saturation is determined by the excitation purity, and depends on the amount of white light mixed with the hue. A pure hue is fully saturated, i.e. no white light mixed in. Hue and saturation together determine the chromaticity for a given colour. Finally, the intensity is determined by the actual amount of light, with more light corresponding to more intense colours[1].
Achromatic light has no colour - its only attribute is quantity or intensity. Greylevel is a measure of intensity. The intensity is determined by the energy, and is therefore a physical quantity. On the other hand, brightness or luminance is determined by the perception of the colour, and is therefore psychological. Given equally intense blue and green, the blue is perceived as much darker than the green. Note also that our perception of intensity is nonlinear, with changes of normalised intensity from 0.1 to 0.11 and from 0.5 to 0.55 being perceived as equal changes in brightnes.Colour depends primarily on the reflectance properties of an object. We see those rays that are reflected, while others are absorbed. However, we also must consider the colour of the light source, and the nature of human visual system. For example, an object that reflects both red and green will appear green when there is green but no red light illuminating it, and conversely it will appear red in the absense of green light. In pure white light, it will appear yellow (= red + green).The pure colours of the spectrum lie on the curved part of the boundary, and a standard white light has colour defined to be near (but not at) the point of equal energy x = y = z = 1/3. Complementary colours, i.e. colours that add to give white, lie on the endpoints of a line through this point. As illustrated in figure 4, all the colours along any line in the chromaticity diagram may be obtained by mixing the colours on the end points of the line. Furthermore, all colours within a triangle may be formed by mixing the colours at the vertices. This property illustrates graphically the fact that all visible colours cannot be obtained by a mix of R, G and B (or any other three visible) primaries alone, since the diagram is not triangular!
full color,pseudo color,color fundamentals,Hue saturation Brightness,color model,RGB color model,CMY and CMYK color model,HSI color model,Coverting RGB to HSI, HSI examples
Color fundamentals and color models - Digital Image ProcessingAmna
This presentation is based on Color fundamentals and Color models.
~ Introduction to Colors
~ Color in Image Processing
~ Color Fundamentals
~ Color Models
~ RGB Model
~ CMY Model
~ CMYK Model
~ HSI Model
~ HSI and RGB
~ RGB To HSI
~ HSI To RGB
Do Not just learn computer graphics an close your computer tab and go away..
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Visit Daroko blog for real IT skills applications,androind, Computer graphics,Networking,Programming,IT jobs Types, IT news and applications,blogging,Builing a website, IT companies and how you can form yours, Technology news and very many More IT related subject.
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Any colour that can be specified using a model will correspond to a single point within the subspace it defines. Each colour model is oriented towards either specific hardware (RGB,CMY,YIQ), or image processing applications (HSI).
About color PPT is giving a introducton on colour, from how we see, waht all guidelines we need to take care while we are designing, how it affects us, what all cultural values it got.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
2. Spectrum of White Light
(Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.
1666 Sir Isaac Newton, 24 year old, discovered white light spectrum.
3. Electromagnetic Spectrum
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Visible light wavelength: from around 400 to 700 nm
1. For an achromatic (monochrome) light source,
there is only 1 attribute to describe the quality: intensity
2.For a chromatic light source, there are 3 attributes to describe
the quality:
Radiance = total amount of energy flow from a light source (Watts)
Luminance = amount of energy received by an observer (lumens)
Brightness = intensity
5. Two Types of Photoreceptors at Retina
– Mesopic vision
• provided at intermediate illumination by both rod and cones
• Rods
– Long and thin
– Large quantity (~ 100 million)
– Provide scotopic vision (i.e., dim light vision or at low illumination)
– Only extract luminance information and provide a general overall picture
• Cones
– Short and thick, densely packed in fovea (center of retina)
– Much fewer (~ 6.5 million) and less sensitive to light than rods
– Provide photopic vision (i.e., bright light vision or at high illumination)
– Help resolve fine details as each cone is connected to its own nerve end
– Responsible for color vision
our interest
(well-lighted display)
6. (Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Sensitivity of Cones in the Human Eye
7millions cones
in a human eye
- 65% sensitive to Red light
- 33% sensitive to Green light
- 2 % sensitive to Blue light
Primary colors:
Defined CIE in 1931
Red = 700 nm
Green = 546.1nm
Blue = 435.8 nm
CIE = Commission Internationale de l’Eclairage
(The International Commission on Illumination)
7. Luminance vs. Brightness
• Luminance (or intensity)
– Independent of the luminance of surroundings
I(x,y,) -- spatial light distribution
V() -- relative luminous efficiency func. of visual system ~ bell shape
(different for scotopic vs. photopic vision;
highest for green wavelength, second for red, and least for blue )
• Brightness
– Perceived luminance
– Depends on surrounding luminance
Same lum.
Different
brightness
Different lum.
Similar
brightness
8. Luminance vs. Brightness (cont’d)
• Example: visible digital watermark
– How to make the watermark
appears the same graylevel
all over the image?
Source:from IBM Watson web page “Vatican Digital Library”
9. Mach Bands
• Visual system tends to undershoot or overshoot around the boundary of regions of different intensities
Demonstrates the perceived brightness is not a function of light intensity
10. Color of Light
• Perceived color depends on spectral content(wavelength
composition)
– e.g., 700nm ~ red.
– “spectral color”
• A light with very narrow bandwidth
• A light with equal energy in all visible bands appears
white
“Spectrum” from http://www.physics.sfasu.edu/astro/color.html
11. Primary and Secondary Colors
Primary
color
Primary
color
Primary
color
Secondary
colors
12. (Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Primary and Secondary Colors (cont.)
Additive primary colors: RGB
use in the case of light sources
such as color monitors
RGB add together to get white
Subtractive primary colors: CMY
use in the case of pigments in
printing devices
White subtracted by CMY to get
Black
13. Example: Seeing Yellow Without Yellow
mix green and red light to obtain perception of
yellow, without shining a single yellow photon
520nm 630nm
570nm
=
14. Hue: dominant color corresponding to a dominant
wavelength of mixture light wave
Relative purity or amount of white light mixed
with a hue (inversely proportional to amount of white
light added)
Intensity
Saturation:
Brightness:
Color Characterization
Hue
Saturation
Chromaticity
amount of red (X), green (Y) and blue (Z) to form any particular
color is called tristimulus.
15. Perceptual Attributes of Color
• Value of Brightness
(perceived luminance)
• Chrominance
– Hue
• specify color tone (redness, greenness, etc.)
• depend on peak wavelength
– Saturation
• describe how pure the color is
• depend on the spread (bandwidth) of light
spectrum
• reflect how much white light is added
• RGB HSV Conversion~ nonlinear
HSV circular cone is from online
documentation of Matlab image
processing toolbox
http://www.mathworks.com/access
/helpdesk/help/toolbox/images/col
or10.shtml
16. (Images from Rafael C. Gonzalez and Richard E.
CIE Chromaticity Diagram
Trichromatic coefficients:
X
X Y Z
x
Y
X Y Z
y
Z
X Y Z
Wood, Digital Image Processing, 2nd Edition.
z
x y z 1
x
y
Points on the boundary are
fully saturated colors
17. CIE Color Coordinates (cont’d)
• CIE XYZ system
– hypothetical primary sources to yield all-positive spectral
tristimulus values
– Y ~ luminance
• Color gamut of 3 primaries
– Colors on line C1 and C2 can be
produced by linear mixture of the two
– Colors inside the triangle gamut
can be reproduced by three primaries
From http://www.cs.rit.edu/~ncs/color/t_chroma.html
18. RGB Color Model
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Purpose of color models: to facilitate the specification of colors in
some standard
RGB color models:
- based on cartesian
coordinate system
19. RGB Color Cube
R = 8 bits
G = 8 bits
B = 8 bits
Color depth 24 bits
= 16777216 colors
Hidden faces
of the cube
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
20. RGB Color Model (cont.)
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Red fixed at 127
21. Safe RGB Colors
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Safe RGB colors: a subset of RGB colors.
There are 216 colors common in most operating systems.
22. RGB Safe-color Cube
The RGB Cube is divided into
6 intervals on each axis to achieve
the total 63 = 216 common colors.
However, for 8 bit color
representation, there are the total
256 colors. Therefore, the remaining
40 colors are left to OS.
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
23. CMY and CMYK Color Models
C = Cyan
M = Magenta
Y = Yellow
K = Black
• Primary colors for pigment
– Defined as one that subtracts/absorbs a
primary color of light & reflects the
other two
• CMY – Cyan, Magenta,Yellow
– Complementary to RGB
– Proper mix of them produces black
Y
1 B
C 1 R
M 1 G
24. HSI Color Model
RGB, CMY models are not good for human interpreting
HSI Color model:
Hue: Dominant color
Saturation: Relative purity (inversely proportional
to amount of white light added)
Intensity: Brightness
Color carrying
information
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
25. Intensity
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Example: HSI Components of RGB Cube
Hue Saturation
RGB Cube
26. Example: HSI Components of RGB Colors
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Hue
Saturation Intensity
RGB
Image
27. Example: Manipulating HSI Components
Hue
Saturation Intensity
RGB
Image Hue
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Saturation
Intensity RGB
Image
30. Summary
• Monochrome human vision
– visual properties: luminance vs. brightness, etc.
– image fidelity criteria
• Color
– Color representations and three primarycolors
– Color coordinates
31. Color Image Processing
There are 2 types of color image processes
1. Pseudocolor image process: Assigning colors to gray
values based on a specific criterion. Gray scale images to be processed
may be a single image or multiple images such as multispectral images
2. Full color image process: The process to manipulate real
color images such as color photographs.
32. (Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Pseudocolor Image Processing
Pseudo color = false color : In some case there is no “color” concept
for a gray scale image but we can assign “false” colors to an image.
Why we need to assign colors to gray scale image?
Answer: Human can distinguish different colors better than different
shades of gray.
33. Intensity Slicing or Density Slicing
C if f (x, y) T
if f (x, y) T
g(x, y)
C2
1
Formula:
C1 = Color No.1
C2 = Color No.2
T
Color
C1
C2
T
Intensity
0 L-1
A gray scale image viewed as a 3D surface.
34. (Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Intensity Slicing Example
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.
35. Multi Level Intensity Slicing Example
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
g(x, y) Ck for lk1 f (x, y) lk
Ck = Color No.k
lk = Threshold level k
An X-ray image of the Picker
Thyroid Phantom.
After density slicing into 8 colors
36. Color Coding Example
Color coded image South America region
Gray
Scale
Color
map
Gray-scale image of average
monthly rainfall.
0
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
10
>20
37. Gray Level to Color Transformation
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Assigning colors to gray levels based on specific mapping functions
Red component
Green component
Blue component
Gray scale image
38. (Images from Rafael C.
Gonzalez and Richard
E. Wood, Digital Image
Processing, 2nd Edition.
Gray Level to Color Transformation Example
An X-ray image of a
garment bag with a
simulated explosive
device
An X-ray image
of a garment bag
Color
coded
images
Transformations
39. (Images from Rafael C.
Gonzalez and Richard
E. Wood, Digital Image
Processing, 2nd Edition.
Gray Level to Color Transformation Example
An X-ray image of a
garment bag with a
simulated explosive
device
An X-ray image
of a garment bag
Color
coded
images
Transformations
40. Pseudocolor Coding Example
Psuedocolor rendition
of Jupiter moon Io
Yellow areas = older sulfur deposits.
Aclose-up
Red areas = material ejected from
active volcanoes.
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
41. Example: Full-Color Image and Various Color Space Components
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Color image
CMYK components
RGB components
HSI components
42. Color Complements
Color complement replaces each color with its opposite color in the
color circle of the Hue component. This operation is analogous to
image negative in a gray scale image.
Color circle
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
43. Color Complement Transformation Example
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
44. Color Slicing Transformation Example
Original image
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
After color slicing
45. Tonal Correction Examples
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
In these examples, only
brightness and contrast are
adjusted while keeping color
unchanged.
This can be done by
using the same transformation
for all RGB components.
Contrast enhancement
Power law transformations
46. Color Balancing Correction Examples
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Color imbalance: primary color components in white area
are not balance. We can measure these components by
using a color spectrometer.
Color balancing can be
performed by adjusting
color components separately
as seen in this slide.
47. Histogram Equalization of a Full-Color Image
Histogram equalization of a color image can be performed by
adjusting color intensity uniformly while leaving color unchanged.
The HSI model is suitable for histogram equalization where only
Intensity (I) component is equalized.
k
k
nj
sk T(rk ) pr (rj )
j0
j0 N
where r and s are intensity components of input and output color image.
48. (Images from Rafael C.
Gonzalez and RichardE.
Wood, Digital Image
Processing, 2nd Edition.
Histogram Equalization of a Full-Color Image
Original image
After histogram
equalization
After increasing
saturation component
49. Color Image Smoothing Example (cont.)
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Color image Red
Green Blue
50. Color Image Smoothing Example (cont.)
Hue Saturation Intensity
Color image
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
HSI Components
51. Color Image Smoothing Example (cont.)
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Smooth all RGB components Smooth only I component of HSI
(faster)
52. Color Image Smoothing Example (cont.)
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Difference between
smoothed results from 2
methods in the previous
slide.
53. Color Image Sharpening
We can do in the same manner as color image smoothing:
1. Per-color-plane method for RGB,CMY images
2. Sharpening only I component of a HSI image
Sharpening all RGB components Sharpening only I component of HSI
54. Color Image Sharpening Example (cont.)
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Difference between
sharpened results from 2
methods in the previous
slide.
55. Gradient of a Color Image
Since gradient is define only for a scalar image, there is no concept
of gradient for a color image. We can’t compute gradient of each
color component and combine the results to get the gradient of a color
image.
Red Green Blue
Edges
We see
4 objects.
We see
2 objects.
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
56. Obtained using
the formula
in the previous
slide
Sum of
gradients of
each color
component
Original
image
2
3
Difference
between
22and 33
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Gradient of a Color Image Example
57. Gradients of each color component
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Red Green Blue
Gradient of a Color Image Example
58. Noise in Color Images
Noise can corrupt each color component independently.
(Images from Rafael C.
Gonzalez and RichardE.
Wood, Digital Image
Processing, 2nd Edition.
Noise is less
noticeable
in a color
image
AWGN =800
2
AWGN =800
2
AWGN =800
2
59. Noise in Color Images
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
Hue Saturation Intensity
60. Color Image Compression
JPEG2000 File
Original image
After lossy compression with ratio 230:1 (Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.