Digital image processing refers to processing digital images using computers. It involves representing images digitally, typically as matrices of pixels and their intensities. Images are acquired using sensors and sampling, which converts a continuous image into discrete pixel values. Common applications of image processing include photography, video, medical imaging, robotics, and more. Color image processing involves representing and processing color images, where color is described by combinations of primary colors like red, green, and blue.
analyze, and enhance digital images. Digital images are representations of visual data in the form of a grid of pixels, with each pixel representing the color and intensity of a small area of the image. Digital image processing techniques can be used to improve the visual quality of an image, extract useful information from it, or enable computers to interpret and understand the content of an image.
analyze, and enhance digital images. Digital images are representations of visual data in the form of a grid of pixels, with each pixel representing the color and intensity of a small area of the image. Digital image processing techniques can be used to improve the visual quality of an image, extract useful information from it, or enable computers to interpret and understand the content of an image.
Digital image processing is the use of algorithms and mathematical models to process digital images. The goal of digital image processing is to enhance the quality of images, extract meaningful information from images, and automate image-based tasks.
Human Visual System in Digital Image Processing.pptGSCWU
A human visual system model (HVS model) is used by image processing, video processing and computer vision experts to deal with biological and psychological processes that are not yet fully understood. Such a model is used to simplify the behaviours of what is a very complex system.
WEBINAR ON FUNDAMENTALS OF DIGITAL IMAGE PROCESSING DURING COVID LOCK DOWN by K.Vijay Anand , Associate Professor, Department of Electronics and Instrumentation Engineering , R.M.K Engineering College, Tamil Nadu , India
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Digital image processing is the use of algorithms and mathematical models to process digital images. The goal of digital image processing is to enhance the quality of images, extract meaningful information from images, and automate image-based tasks.
Human Visual System in Digital Image Processing.pptGSCWU
A human visual system model (HVS model) is used by image processing, video processing and computer vision experts to deal with biological and psychological processes that are not yet fully understood. Such a model is used to simplify the behaviours of what is a very complex system.
WEBINAR ON FUNDAMENTALS OF DIGITAL IMAGE PROCESSING DURING COVID LOCK DOWN by K.Vijay Anand , Associate Professor, Department of Electronics and Instrumentation Engineering , R.M.K Engineering College, Tamil Nadu , India
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
2. Image Processing Books
• Gonzalez, R. C. and Woods, R. E., "Digital
Image Processing", Prentice Hall.
• Jain, A. K., "Fundamentals of Digital Image
Processing", PHI Learning, 1
st
Ed.
• Bernd, J., "Digital Image Processing", Springer,
6
th
Ed.
• Burger, W. and Burge, M. J., "Principles of
Digital Image Processing", Springer
• Scherzer, O., " Handbook of Mathematical
Methods in Imaging", Springer
Sunday, December 18, 2022 2
3. Why we need Image Processing?
• Improvement of pictorial information for
human perception
• Image processing for autonomus machine
applications
• Efficient storage and transmission
Sunday, December 18, 2022 3
4. What is digital image processing?
• An image may be defined as a two dimensional
function f(x,y), where ‘x’ and ‘y’ are spatial(plane)
coordinates and the amplitude of ‘f’ at any pair of
coordinates (x,y) is called the intensity or gray
level of the image at that point.
• When x,y, and the amplitude values of ‘f’ are all
finite, descrete quantities we call the image a
digital image.
• The field of digital image processing refers to
processing digital images by means of digital
computers.
Sunday, December 18, 2022 4
5. What is digital image processing?
(Cont…)
Sunday, December 18, 2022 5
6. Image Processing Applications
• Automobile driver assistance
– Lane departure warning
– Adaptive cruise control
– Obstacle warning
• Digital Photography
– Image Enhancement
– Compression
– Color manipulation
– Image editing
– Digital cameras
• Sports analysis
– sports refereeing and commentary
– 3D visualization and tracking sports actions
Sunday, December 18, 2022 6
7. Image Processing Applications(Cont…)
• Film and Video
– Editing
– Special effects
• Image Database
– Content based image retrieval
– visual search of products
– Face recognition
• Industrial Automation and Inspection
– vision-guided robotics
– Inspection systems
• Medical and Biomedical
– Surgical assistance
– Sensor fusion
– Vision based diagnosis
• Astronomy
– Astronomical Image Enhancement
– Chemical/Spectral Analysis
Sunday, December 18, 2022 7
9. Brief History of IP
• In 1920s, submarine cables were used to transmit
digitized newspaper pictures between London &
New York – using Bartlane cable picture
transmission System.
• Specialized printing equipments(eg. Telegraphic
printer) used to code the picture for cable
transmission and its reproduction on the
receiving end.
• In 1921, printing procedure was changed to
photographic reproduction from tapes perforated
at telegraph receiving terminals.
• This improved both tonal quality & resolution.
Sunday, December 18, 2022 9
11. Brief History of IP(Cont…)
• Bartlane system was capable of coding 5 distinct
brightness levels. This was increased to 15 levels
by 1929.
• Improvement of processing techniques continued
for next 35 years .
• In 1964 computer processing techniques were
used to improve the pictures of moon tranmitted
by ranger 7 at Jet Propulsion Laboratory.
• This was the basis of modern Image Processing
techniques.
Sunday, December 18, 2022 11
16. Image Sensing and Acquisition(Cont…)
• Image acquisition using a single sensor
Sunday, December 18, 2022 16
17. Image Sensing and Acquisition(Cont…)
• Using sensor strips
Sunday, December 18, 2022 17
18. Image Representation
Sunday, December 18, 2022 18
x
y
IMAGE
An image is a 2-D light intensity function F(X,Y).
F(X,Y) = R(X,Y)* I(X,Y) , where
R(X,Y) = Reflectivity of the surface of the corresponding image point.
I(X,Y) = Intensity of the incident light.
A digital image F(X,Y) is discretized both in spatial coordinates and brightness.
It can be considered as a matrix whose row, column indices specify a point in
the image & the element value identifies gray level value at that point known
as pixel or pels.
19. Image Representation (Cont..)
Sunday, December 18, 2022 19
(0,0) (0,1) ... (0, 1)
(1,0) (1,1) ... (1, 1)
( , )
... ... ... ...
( 1,0) ( 1,1) ... ( 1, 1)
f f f N
f f f N
f x y
f M f M f M N
Image Representation in Matrix form
22. Image Representation (Cont..)
Sunday, December 18, 2022 22
( , ) ( , ) ( , )
( , ): intensity at the point ( , )
( , ): illumination at the point ( , )
(the amount of source illumination incident on the scene)
( , ): reflectance/transmissivity
f x y i x y r x y
f x y x y
i x y x y
r x y
at the point ( , )
(the amount of illumination reflected/transmitted by the object)
where 0 < ( , ) < and 0 < ( , ) < 1
x y
i x y r x y
23. Image Representation (Cont..)
• By theory of real numbers :
Between any two given points there are infinite
number of points.
• Now by this theory :
An image should be represented by infinite
number of points.
Each such image point may contain one of the
infinitely many possible intensity/color values
needing infinite number of bits.
Obviously such a representation is not possible in
any digital computer.
Sunday, December 18, 2022 23
24. Image Sampling and Quantization
• By above slides we came to know that we need to
find some other way to represent an image in
digital format.
• So we will consider some discrete set of points
known as grid and in each rectangular grid
consider intensity of a particular point. This
process is known as sampling.
• Image representation by 2-d finite matrix –
Sampling
• Each matrix element represented by one of the
finite set of discrete values - Quantization
Sunday, December 18, 2022 24
35. Colour Image Processing
• Why we need CIP when we get information from
black and white image itself?
1. Colour is a very powerful descriptor & using the
colour information we can extract the objects of
interest from an image very easily which is not so easy
in some cases using black & white pr simple gray level
image.
2. Human eyes can distinguish between thousands of
colours & colour shades whereas when we talk about
only black and white image or gray scale image we
can distinguish only about dozens of intensity
distinguishness or different gray levels.
Sunday, December 18, 2022 35
36. Color Image processing(Cont…)
• The color that human perceive in an object =
the light reflected from the object
Illumination source
scene
reflection
Humen eye
37. Colour Image Processing(Cont...)
• In CIP there are 2 major areas:
1.FULL CIP : Image which are acquired by full colour
TV camera or by full color scanner, than, you find
that all the colour you perceive they are present in
the images.
2.PSEUDO CIP : Is a problem where we try to assign
certain colours to a range of gray levels. Pseudo CIP is
mostly used for human interpretation.
So here it is very difficult to distinguish between two
ranges which are very nearer to each other or gray
intensity value are very near to each other.
Sunday, December 18, 2022 37
38. Colour Image Processing(Cont...)
• Problem with CIP
Interpretation of color from human eye is a
psycophisological problem and we have not yet
been fully understand what is the mechanism by
which we really interpret a color.
Sunday, December 18, 2022 38
40. Colour Image Processing(Cont...)
• We can perceive the color depending on the nature of light
which is reflected by the object surface.
• Spectrum of light or spectrum of energy in the visible range that
we are able to perceive a color(400 nm to 700 nm)
Sunday, December 18, 2022 40
41. Colour Image Processing(Cont...)
• Attribute of Light
Achromatic Light : A light which has no color
component i.e., the only attribute which
describes that particular light is the intensity of
the light.
Chromatic Light : Contain color component.
• 3 quantities that describe the quality of light:
Radiance
Luminance
Brightness
Sunday, December 18, 2022 41
42. Colour Image Processing(Cont...)
• Radiance : Total amount of energy which comes
out of a light (Unit : watts)
• Luminance : Amount of energy that is perceived
by an observer (Unit : Lumens)
• Brightness : It is a subjective thing. Practically we
can’t measure brightness.
We have 3 primary colors:
Red
Blue
Green
Sunday, December 18, 2022 42
43. Colour Image Processing(Cont...)
• Newton discovered 7 different color but only 3
colors i.e., red, green and blue are the primary
colors. Why?
Because by mixing these 3 colors in some proportion
we can get all other colors.
There are around 6-7 millions cone cells in our eyes
which are responsible for color sensations.
Around 65% cone cells are sensitive to red color.
Around 33% cone cells are sensitive to green color.
Around 2% cone cells are sensitive to blue color.
Sunday, December 18, 2022 43
44. Colour Image Processing(Cont...)
• According to CIE standard
Red have wavelength : 700 nm
Green have wavelength : 546.1 nm
Blue have wavelength : 435.6 nm
But, practically :
Red is sensitive to 450 nm to 700 nm
Green is sensitive to 400 nm to 650 nm
Blue is sensitive to 400 nm to 550 nm
Sunday, December 18, 2022 44
45. Colour Image Processing(Cont...)
• Note : In practical no single wavelength can
specify any particular color.
• By spectrum color also we can see that there is
no clear cut boundaries between any two color.
• One color slowly or smoothly get merged into
another color i.e., there is no clear cut boundary
between transition of color in spectrum.
• So, we can say a band of color give red, green
and blue color sensation respectively.
Sunday, December 18, 2022 45
46. Colour Image Processing(Cont...)
• Mixing of Primary color generates the secondary
colors i.e.,
RED+BLUE=Magenta
GREEN+BLUE = Cyan
RED+GREEN = yellow
• Here red, green and blue are the primary color
and magenta, cyan and yellow are the secondary
color.
• Pigments : The primary color of pigment is
defined as wavelength which are absorbed by the
pigment and it reflect other wavelength.
Sunday, December 18, 2022 46
47. Colour Image Processing(Cont...)
• Primary color of light should be opposite of primary color of
pigment i.e., magenta , cyan and yellow are primary color of
pigment.
• If we mix red, green and blue color in appropriate proportion
we get white light and similarly when we mix magenta, cyan
and yellow we get black color.
Sunday, December 18, 2022 47
48. Colour Image Processing(Cont...)
• For hardware i.e., camera, printer, display
device, scanner this above concept of color is
used i.e., concept of primary color
component.
• But when we perceive a color for human
beings we don’t think that how much
red,green and blue components are mixed in
that particular color.
• So the way by which we human differentiate
or recognize or distinguish color are :
Brightness, Hue and Saturation.
Sunday, December 18, 2022 48
49. Colour Image Processing(Cont...)
• Spectrum colors are not diluted i.e., spectrum
colors are fully saturated . It means no white
light or white component are added to it.
• Example: Pink is not spectrum color.
Red + white = pink
Here red is fully saturated.
• So, Hue+Saturation indicates chromaticity of
light and Brightness gives some sentation of
intensity.
Sunday, December 18, 2022 49
50. Colour Image Processing(Cont...)
• Brightness : Achromatic notion of Intensity.
• Hue : It represents the dominant wavelength
present in a mixture of colors.
• Saturation : eg., when we say color is red i.e.,
we may have various shades of red. So
saturation indicates what is the purity of red
i.e., what is the amount of light which has
been mixed to that particular color to make it
a diluted one.
Sunday, December 18, 2022 50
51. Colour Image Processing(Cont...)
• The amount of red, green and blue
component is needed to get another color
component is known as tristimulus.
• Tristimulus = (X,Y,Z)
• Chromatic cofficient for red = X/(X+Y+Z) , for
green = Y/(X+Y+Z) , for blue = Z/(X+Y+Z).
• Here X+Y+Z=1
• So any color can be specified by its chromatic
cofficient or a color can be specified by a
chromaticity diagram.
Sunday, December 18, 2022 51
52. Colour Image Processing(Cont...)
• Here Z = 1-(X+Y) , In chromaticity diagram around the
boundary we have all the color of the spectrum colors and
point of equal energy is : white color.
Sunday, December 18, 2022 52
53. Colour Image Processing(Cont...)
• Color Models : A coordinate system within
which a specified color will be represented by
a single point.
• RGB , CMY , CMYK : Hardware oriented
• HSI : Hue , Saturation and Intensity :
Application oriented / Perception oriented
• In HSI model : I part gives you gray scale
information. H & S taken together gives us
chromatic information.
Sunday, December 18, 2022 53
54. Colour Image Processing(Cont...)
• RGB Color Model : Here a color model is represented
by 3 primary colors i.e., red , green and blue.
• In RGB color model we can have 224 different color
combinations but practically 216 different colors can
be represented by RGB model.
• RGB color model is based on Cartesian coordinate
system.
• This is an additive color model
• Active displays, such as computer monitors and
television sets, emit combinations of red, green and
blue light.
Sunday, December 18, 2022 54
58. Colour Image Processing(Cont...)
• CMY Color Model : secondary colors of light, or
primary colors of pigments & Used to generate
hardcopy output
Sunday, December 18, 2022 58
Source: www.hp.com
Passive displays, such as colour inkjet printers, absorb light instead of
emitting it. Combinations of cyan, magenta and yellow inks are used. This
is a subtractive colour model.
59. Colour Image Processing(Cont...)
• Equal proportion of CMY gives a muddy black
color i.e., it is not a pure black color. So, to get
pure black color with CMY another
component is also specified known as Black
component i.e., we get CMYK model.
• In CMYK “K” is the black component.
Sunday, December 18, 2022 59
B
G
R
Y
M
C
1
1
1
60. Colour Image Processing(Cont...)
• HSI Color Model (Based on human perception of
colors )
• H = What is the dominant specified color present in a
particular color. It is a subjective measure of color.
• S = How much a pure spectrum color is really diluted
by mixing white color to it i.e., Mixing more “white”
with a color reduces its saturation.
If we mix white color in different proportion with a
color we get different shades of that color.
• I = Chromatic notation of brightness of black and
white image i.e., the brightness or darkness of an
object.
Sunday, December 18, 2022 60
64. Colour Image Processing(Cont...)
• Pseudo-color Image Processing
Pseudo-Color = False Color
Assign colors to gray values based on a specified
criterion
For human visualization and interpretation of
gray-scale events
Intensity slicing
Gray level to color transformations
Sunday, December 18, 2022 64
65. Colour Image Processing(Cont...)
• Pseudo-color Image Processing(cont…)
Intensity slicing
Here first consider an intensity image to be a 3D plane.
Place a plane which is parallel to XY plane(it will slice the
plane into two different hubs).
We can assign different color on two different sides of
the plane i.e., any pixel whose intensity level is above
the plane will be coded with one color and any pixel
below the plane will be coded with the other.
Level that lie on the plane itself may be arbitrarily
assigned one of the two colors.
Sunday, December 18, 2022 65
67. Colour Image Processing(Cont...)
Intensity slicing
Let we have total ‘L’ number of intensity values: 0 to (L-1)
L0 corresponds to black [ f(x , y) = 0]
LL-1 corresponds to white [ f(x , y) = L-1]
Suppose ‘P’ number of planes perpendicular to the
intensity axis i.e., they are parallel to the image plane and
these planes will be placed at the intensity values given
by L1,L2,L3,………,LP.
Where , 0< P < L-1.
Sunday, December 18, 2022 67
68. Colour Image Processing(Cont...)
• Intensity slicing
The P planes partition the gray scale(intensity)
into (P+1) intervals, V1,V2,V3,………,VP+1.
Color assigned to location (x,y) is given by the
relation
f(x , y) = Ck if f(x , y) ∈ Vk
Sunday, December 18, 2022 68
71. Colour Image Processing(Cont...)
• Intensity slicing
Give ROI(region of interest) one color and rest part
other color
Keep ROI as it is and rest assign one color
Keep rest as it is and give ROI one color
Sunday, December 18, 2022 71
75. Colour Image Processing(Cont...)
• Pseudo-coloring is also used from gray to color
image transformation.
• Gray level to color transformation
Sunday, December 18, 2022 75
76. Colour Image Processing(Cont...)
• Gray level to color transformation
fR(X,Y) = f(x,y)
fG(X,Y) = 0.33f(x,y)
fB(X,Y) = 0.11f(x,y)
Combining these 3 planes we get the pseudo
color image.
Application of Pseudo CIP : Machine using at
railways and airport for bag checking.
Sunday, December 18, 2022 76
79. Image Enhancement
• Intensity Transformation Functions
• Enhancing an image provides better contrast and a more
detailed image as compare to non enhanced image.
Image enhancement has very applications. It is used to
enhance medical images, images captured in remote
sensing, images from satellite e.t.c
• The transformation function has been given below
s = T ( r )
• where r is the pixels of the input image and s is the pixels
of the output image. T is a transformation function that
maps each value of r to each value of s.
Sunday, December 18, 2022 79
80. Image Enhancement(Cont…)
• Image enhancement can be done through gray
level transformations which are discussed below.
• There are three basic gray level transformation.
• Linear
• Logarithmic
• Power – law
Sunday, December 18, 2022 80
81. Image Enhancement(Cont…)
• Linear Transformation
Linear transformation includes simple identity and negative
transformation.
Identity transition is shown by a straight line. In this transition,
each value of the input image is directly mapped to each other
value of output image. That results in the same input image
and output image. And hence is called identity transformation.
• Negative Transformation
The second linear transformation is negative transformation,
which is invert of identity transformation. In negative
transformation, each value of the input image is subtracted
from the L-1 and mapped onto the output image.
Sunday, December 18, 2022 81
84. Image Enhancement(Cont…)
• Logarithmic Transformations
The log transformations can be defined by this
formula
s = c log(r + 1).
Where s and r are the pixel values of the output
and the input image and c is a constant. The value
1 is added to each of the pixel value of the input
image because if there is a pixel intensity of 0 in
the image, then log (0) is equal to infinity. So 1 is
added, to make the minimum value at least 1.
Sunday, December 18, 2022 84
85. Image Enhancement(Cont…)
• Logarithmic Transformations
In log transformation we decrease the dynamic range of a
particular intensity i.e., here intensity of the pixels are
increased which we require to get more information. The
maximum information is contained in the center pixel.
Log transformation is mainly applied in frequency domain.
Sunday, December 18, 2022 85
87. Image Enhancement(Cont…)
• Power – Law transformations
• This symbol γ is called gamma, due to which this
transformation is also known as gamma
transformation.
Sunday, December 18, 2022 87
s = crγ, c,γ –positive constants
curve the grayscale components either to brighten
the intensity (when γ < 1) or darken the intensity
(when γ > 1).
89. Image Enhancement(Cont…)
• Power – Law transformations
• Variation in the value of γ varies the enhancement
of the images. Different display devices / monitors
have their own gamma correction, that’s why they
display their image at different intensity.
• This type of transformation is used for enhancing
images for different type of display devices. The
gamma of different display devices is different.
For example Gamma of CRT lies in between of 1.8
to 2.5, that means the image displayed on CRT is
dark.
Sunday, December 18, 2022 89
90. Image Enhancement(Cont…)
• Power – Law transformations
Gamma Correction
Different camera or video recorder devices do not
correctly capture luminance. (they are not linear)
Different display devices (monitor, phone screen, TV) do
not display luminance correctly neither. So, one needs to
correct them, therefore the gamma correction function
is needed. Gamma correction function is used to correct
image's luminance.
s=cr^γ
s=cr^(1/2.5)
Sunday, December 18, 2022 90
94. Image Enhancement(Cont…)
• Contrast stretching
Aims increase the dynamic range of the gray
levels in the image being processed.
Contrast stretching is a process that expands the
range of intensity levels in a image so that it
spans the full intensity range of the recording
medium or display device.
Contrast-stretching transformations increase the
contrast between the darks and the lights
Sunday, December 18, 2022 94
96. Image Enhancement(Cont…)
• Contrast stretching
The locations of (r1,s1) and (r2,s2) control the shape of
the transformation function.
– If r1= s1 and r2= s2 the transformation is a linear
function and produces no changes.
– If r1=r2, s1=0 and s2=L-1, the transformation becomes
a thresholding function that creates a binary image.
– Intermediate values of (r1,s1) and (r2,s2) produce
various degrees of spread in the gray levels of the
output image, thus affecting its contrast.
– Generally, r1≤r2 and s1≤s2 is assumed.
Sunday, December 18, 2022 96
108. Image Enhancement(Cont…)
• Intensity-level slicing
Highlighting a specific range of gray levels in an
image.
One way is to display a high value for all gray levels
in the range of interest and a low value for all
other gray levels (binary image).
The second approach is to brighten the desired
range of gray levels but preserve the background
and gray-level tonalities in the image
Sunday, December 18, 2022 108
110. Image Enhancement(Cont…)
• Bit-Plane Slicing
• To highlight the contribution made to the total
image appearance by specific bits.
– i.e. Assuming that each pixel is represented by 8 bits,
the image is composed of 8 1-bit planes.
– Plane 0 contains the least significant bit and plane 7
contains the most significant bit.
– Only the higher order bits (top four) contain visually
significant data. The other bit planes contribute the
more subtle details.
– Plane 7 corresponds exactly with an image thresholded
at gray level 128.
Sunday, December 18, 2022 110
112. Image Enhancement(Cont…)
• Histogram Processing
Two Types : (a). Histogram Stretching (b). Histogram
equalization
Histogram Stretching
Contrast is the difference between maximum and
minimum pixel intensity.
Pictorial view to represent the distribution of pixel which
tell frequency of pixel.
Sunday, December 18, 2022 112
113. Image Enhancement(Cont…)
• The histogram of digital image with gray values
is the discrete function
Sunday, December 18, 2022 113
1
1
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,
,
L
r
r
r
n
n
r
p k
k
)
(
nk: Number of pixels with gray value rk
n: total Number of pixels in the image
The function p(rk) represents the fraction of the total
number of pixels with gray value rk.
The shape of a histogram provides useful information for
contrast enhancement.
117. Image Enhancement(Cont…)
• Histogram Stretching (cont…)
• In the above example (0,8) is smin and smax respectively and
rmin = 0 , rmax = 4 is given.
• S-0 = (8 – 0) / (4 – 0) * (r – 0)
• s=(8/4) r
• S= 2r
• Now we have a relation between r and s.
• So get different values of ‘s’ for given rmin to rmax .
Sunday, December 18, 2022 117
121. Image Enhancement(Cont…)
• Histogram Equalization
– Recalculate the picture gray levels to make the
distribution more equalized
– Used widely in image editing tools and computer
vision algorithms
– Can also be applied to color images
Sunday, December 18, 2022 121
122. Objective of histogram equalization
• We want to find T(r) so that
Ps(s) is a flat line.
Historgram, color v.4e
122
sk
rk
L-1
L-1 r
T(r)
0
Objective:
To find the
Relation s=T(r)
Pr(r)
r
Ps(s)=a constant
s
L-1
L-1
L-1
Equalized distribution
Input random distribution
The original image
The probability of
these levels are lower)
The probability of
these levels are higher
The probability of all
levels are the same
In Ps(s)
s=T(r)
123. we want to prove ps(s)= constant
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124. Image Enhancement(Cont…)
• Histogram Equalization
Sunday, December 18, 2022 124
• Let rk, k[0..L-1] be intensity levels and let p(rk) be its
normalized histogram function.
• Histogram equalization is applying the transformation of
‘r’ to get ‘s’ where ‘r’ belongs to 0 to L-1.
• As, T(r) is continuous & differentiable
ʃPss ds=ʃprr dr =1
differentiating w.r.t ‘s’ we get :
125. Image Enhancement(Cont…)
• Histogram Equalization(cont…)
So, e.q. (1)
The transformation function T(r) for histogram
equalization is :
Differentiate w.r.t ‘r’ :
As we know, , SO,
From eq.(1) we get, which is a constant.
Sunday, December 18, 2022 125
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126. Histogram Equalization : Discrete form for
practical use
• From the continuous form (1) to discrete form
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126
127. Histogram Equalization - Example
• Let f be an image with size 64x64 pixels and L=8 and let f has the intensity
distribution as shown in the table
p r(rk )=nk/MN
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round the values to the nearest integer
131. Filtering
• Image filtering is used to:
Remove noise
Sharpen contrast
Highlight contours
Detect edges
Image filters can be classified as linear or nonlinear.
Linear filters are also know as convolution filters as
they can be represented using a matrix multiplication.
Thresholding and image equalisation are examples of
nonlinear operations, as is the median filter.
Sunday, December 18, 2022 131
132. Filtering(cont…)
• There are two types of processing:
• Point Processing (eg. Histogram equalization)
• Mask Processing
Two types of filtering methods:
• Smoothing
Linear (Average Filter) and Non-Linear (Median
Filter)
• Sharpening
Laplacian
Gradient
Sunday, December 18, 2022 132
136. Filtering(Cont…)
• A filtering method is linear when the output is a
weighted sum of the input pixels. Eg. Average filter
• Methods that do not satisfy the above property are
called non-linear. Eg. Median filter
• Average (or mean) filtering is a method of ‘smoothing’
images by reducing the amount of intensity variation
between neighbouring pixels.
• The average filter works by moving through the image
pixel by pixel, replacing each value with the average value
of neighbouring pixels, including itself.
Sunday, December 18, 2022 136
143. Filtering(Cont…)
• When we apply average filter noise is removed
but blurring is introduced and to remove blurring
we use weighted filter.
Sunday, December 18, 2022 143
144. Filtering(Cont…)
• Median Filter (non-linear filter)
• Very effective in removing salt and pepper or impulsive noise
while preserving image detail
• Disadvantages: computational complexity, non linear filter
• The median filter works by moving through the image pixel by
pixel, replacing each value with the median value of
neighbouring pixels.
• The pattern of neighbours is called the "window", which
slides, pixel by pixel over the entire image 2 pixel, over the
entire image.
• The median is calculated by first sorting all the pixel values
from the window into numerical order, and then replacing the
pixel being considered with the middle (median) pixel value.
Sunday, December 18, 2022 144
151. Filtering(Cont…)
Sharpening(high pass filter) is performed by noting only the
gray level changes in the image that is the differentiation.
• Sharpening is used for edge detection ,line detection, point
detection and it also highlight changes.
Operation of Image Differentiation
• Enhance edges and discontinuities (magnitude of output gray
level >>0)
• De-emphasize areas with slowly varying gray-level values
(output gray level: 0)
Mathematical Basis of Filtering for Image Sharpening
• First-order and second-order derivatives
• Approximation in discrete-space domain
• Implementation by mask filtering
Sunday, December 18, 2022 151
152. Filtering(Cont…)
Common sharpening filters:
• Gradient (1st order derivative)
• Laplacian (2nd order derivative)
• Taking the derivative of an image results in sharpening
the image.
• The derivative of an image (i.e., 2D function) can be
computed using the gradient.
Sunday, December 18, 2022 152
153. Filtering(Cont…)
Gradient (rotation variant or non-isotropic)
Sunday, December 18, 2022 153
or
Sensitive to
vertical
edges
Sensitive to
horizontal
edges
156. Filtering(Cont…)
• Laplacian(rotation invariant or isotropic)
Sunday, December 18, 2022 156
(b)Extended
Laplacian
mask to
increase
sharpness
and it covers
diagonal
also, so ,
provide good
results.
157. Image Transforms
• Many times, image processing tasks are best
performed in a domain other than the spatial
domain.
• Key steps
(1) Transform the image
(2) Carry the task(s) in the transformed domain.
(3) Apply inverse transform to return to the spatial
domain.
158. Math Review - Complex numbers
• Real numbers:
1
-5.2
• Complex numbers
4.2 + 3.7i
9.4447 – 6.7i
-5.2 (-5.2 + 0i)
1
i
We often denote in EE i by j
159. Math Review - Complex numbers
• Complex numbers
4.2 + 3.7i
9.4447 – 6.7i
-5.2 (-5.2 + 0i)
• General Form
Z = a + bi
Re(Z) = a
Im(Z) = b
• Amplitude
A = | Z | = √(a2 + b2)
• Phase
= Z = tan-1(b/a)
Real and imaginary parts
160. Math Review – Complex Numbers
• Polar Coordinate
Z = a + bi
• Amplitude
A = √(a2 + b2)
• Phase
= tan-1(b/a)
a
b
A
161. Math Review – Complex Numbers and
Cosine Waves
• Cosine wave has three properties
– Frequency
– Amplitude
– Phase
• Complex number has two properties
– Amplitude
– Wave
• Complex numbers to represent cosine waves at varying frequency
– Frequency 1: Z1 = 5 +2i
– Frequency 2: Z2 = -3 + 4i
– Frequency 3: Z3 = 1.3 – 1.6i
Simple but great idea !!
162. Fourier Transforms & its Properties
• Jean Baptiste Joseph Fourier (1768-1830)
Sunday, December 18, 2022 162
• Had crazy idea (1807):
• Any periodic function can be
rewritten as a weighted sum of
Sines and Cosines of different
frequencies.
• Don’t believe it?
– Neither did Lagrange,
Laplace, Poisson and other
big wigs
– Not translated into English
until 1878!
• But it’s true!
– called Fourier Series
– Possibly the greatest tool
used in Engineering
163. Fourier Transforms & its Properties
• In image processing:
– Instead of time domain: spatial domain (normal image
space)
– frequency domain: space in which each image value at
image position F represents the amount that the
intensity values in image I vary over a specific distance
related to F
Sunday, December 18, 2022 163
164. Fourier Transforms & its Properties
• Fourier Transforms & Inverse Fourier Transforms
Sunday, December 18, 2022 164
168. Fourier Transforms & its Properties
• As we deal with 2-d discrete images so we need to
discuss 2-D discrete Fourier Transforms.
Sunday, December 18, 2022 168
169. Fourier Transforms & its Properties
• Inverse F.T
Sunday, December 18, 2022 169
170. Fourier Transforms & its Properties
• If the image is represented as square array i.e.,
M=N than F.T and I.F.T is given by equation:
Sunday, December 18, 2022 170
174. Fourier Transforms & its Properties
• Periodicity: The DFT and its inverse are periodic wit
period N.
Sunday, December 18, 2022 174
175. Fourier Transforms & its Properties
Sunday, December 18, 2022 175
• Scaling: If a signal is multiply by a scalar quantity ‘a’
than its Fourier Transformation is also multiplied by
same scalar quantity ‘a’.
176. Fourier Transforms & its Properties
• Distributivity:
Sunday, December 18, 2022 176
but …
177. Fourier Transforms & its Properties
• Average:
Sunday, December 18, 2022 177
Average:
F(u,v) at u=0, v=0:
So:
182. Frequency Domain Filters
• Low pass filter: it allows low frequency range signal to pass as
output.(Useful for noise suppression)
• High pass filter: it allows high pass frequency range to pass as
output. (Useful for edge detection)
• D(u,v) is distance of (u , v) in frequency domain from the origin of
the frequency rectangle.
• Do implies that all signals lies in this range i.e., D(u,v)<= Do all
low pass frequency to pass to the output and rest are not allowed
to pass as output.
185. Frequency Domain Filters
• In the above example, for the same cut of
frequency the blurring is more in Ideal low
pass filter than in butterworth filter and as cut
of frequency increases the number of
undesired lines are increased in Ideal low pass
filter than in butterworth filter.