DIP Notes Unit-1 PPT , engineering, computer Science

6CS3-01: Digital Image Processing
UNIT 1
Topic: Digital Image Representation, Sampling & Quantization, Steps in
Image Processing, Image acquisition, Color Image Representation
Presented By: Yashika Saini, Assistant Professor
Department of Computer Science Engineering
Arya Institute of Engineering & Technology, Jaipur

Introduction to Image Processing
• Image is defined as a 2-dimensional function,
F(x, y), Where x & y are spatial coordinates, F
is intensity or amplitude.
• An image is a projection of a 3D scene onto a
2D projection plane.
• Types of Images:
1. Analog Image
2. Digital Image

• Analog Image: An analog image can be
represented as a continous range of values
representing position (x, y) and Intensity (F).
• Digital Image: When x, y & F are all finite
discrete quantities, image is digital image.
• A digital image is composed of picture
elements called pixels.
• Digital Image Processing: The analysis and
manipulation of a digitized image, in order to
improve its quality is called digital image
processing.

Applications of Digital Image Processing
1. Gamma-Ray Imaging: Major uses of imaging based
on gamma rays include nuclear medicine and
astronomical observations.

2. X-Ray Imaging:
• X-rays are among the oldest sources of EM radiation used for
imaging.
• X-rays for medical and industrial imaging are generated using
an X-ray tube, which is a vacuum tube with a cathode and
anode

3. Imaging in the Ultraviolet Band:
• They include lithography, industrial inspection,
microscopy, lasers, biological imaging, and
astronomical observations.
• Ultraviolet light is used in fluorescence microscopy,
one of the fastest growing areas of microscopy.

4. To improve Quality, remove noise etc.
5. Imaging in the Microwave Band:
• The principal application of imaging in the
microwave band is radar.
• The unique feature of imaging radar is its ability to
collect data over virtually any region at any time,
regardless of weather or ambient lighting conditions.

Components of Image Processing System

Image Representation
Before we discuss image acquisition recall that a digital
image is composed of M rows and N columns of
pixels each storing a value.
Pixel values are most
often grey levels in the
range 0-255(black-white).
We will see later on
that images can easily
be represented as
matrices
col
row
f (row, col)

Image Sensing
• Incoming energy lands on a sensor material
responsive to that type of energy and this generates a
voltage.
• Collections of sensors are arranged to capture images.
1. Single Sensor
2. Line Sensors 3. Array Sensors

Image Acquisition
Images are typically generated by illuminating a
scene and absorbing the energy reflected by the
objects in that scene.
– Typical notions of
illumination and scene
can be way off:
• X-rays of a skeleton
• Ultrasound of an
unborn baby
• Electro-microscopic
images of molecules

Image Acquisition
Image Acquisition can be done in three ways:
1. Image Acquisition using Single Sensor.
2. Image Acquisition using Sensor Strip.
3. Image Acquisition using sensor arrays.

Images are analog
• Notice that we defined images as functions in a
continuous domain.
• Images are representations of an analog world.
• Hence, as with all digital signal processing,
we need to digitize our images.

Digitalization
• Digitalization of an analog signal involves
two operations:
• Sampling, and
• Quantization
• Both operations correspond to discretization of
a quantity, but in different domains.

Image Sampling & Quantization
• To convert analog image into digital image we have
two intermediate steps.
• Digitizing the coordinates values (x,y) is called
sampling and digitizing the amplitude values (F) is
called quantization.

Representing Digital Images
• Let f(s,t) be a continuous image function, Where s & t
are continuous variables.
• We convert this function into a digital image by
sampling and quantization.
• Suppose that we sample the continuous image into a
2-D array, f(x,y) , having M rows and N columns,
where (x,y) are discrete coordinates.
• Where, x = 0, 1, 2,….., M -1
y = 0, 1, 2,…..., N-1.

Representing Digital Images
• There are three basic ways to represent f(x,y).

Digital Image Representation
• Figure shows a plot of the function, with two axes determining
spatial location and the third axis being the values of f
(intensities) as a function of the two spatial variables x and y.
• This type of representation is not preferable because in dealing
with complex images, interpolation becomes very difficult.
Figure: Image plotted as a surface.

• It shows f(x,y) as it would appear on a monitor or
photograph. Here, the intensity of each point is
proportional to the value of f at that point.
Figure: Image displayed as a visual intensity array

• In this figure, there are only three discrete
intensity values. If the intensity is normalized
to the interval [0, 1], then each point in the
image has the value 0, 0.5, or 1.
• A monitor or printer simply converts these
three values to black, gray, or white,
respectively.

• The third representation is simply to display the numerical
values of f(x,y) as an array (matrix).
• When developing algorithms, this representation is quite
useful when only parts of the image are printed and analyzed
as numerical values.
Figure: 2-D numerical array (0, .5,
and 1) represent black, gray, and
white.

• In equation form, we write the representation
of an M x N numerical array as
• Each element of this matrix is called an image
element, picture element, pixel or pel.

• M & N should be positive integers. But the
number of intensity levels L should
• Where, K is integer.
• Bits required to store a digitized image is
b=M x N x K
When M = N (No. of rows= No. of column) , then

Image Acquisition
• The image is captured by a sensor (eg. Camera), and
digitized if the output of the camera or sensor is not
already in digital form, using analogue-to-digital
convertor.

Image Enhancement
• The process of manipulating an image so that the
result is more suitable than the original for
specific applications.
• The idea behind enhancement techniques is to
bring out details that are hidden, or simple to
highlight certain features of interest in an image.

Image Restoration
• Improving the appearance of an image.
• Tend to be mathematical or probabilistic
models. Enhancement, on the other hand, is
based on preferences
regarding
human
what
subjective
constitutes a “good”
enhancement result.

Color Image Processing
• Use the color of the image to extract features
of interest in an image.
• It includes color modeling and processing in
digital domain.

Wavelets & Multi-Resolution
• Wavelets are small waves of limited duration
which are used to calculate wavelet transform
which provides time frequency information.
• Wavelets lead to multiresolution processing in
which images are represented in various
degrees of resolution.

Compression
• Techniques for reducing the storage required to
save an image or the bandwidth required to
transmit it.

Morphological Processing
• Tools for extracting image components that are
useful in the representation and description of
shape.
• In this step, there would be a transition from
processes that output images, to processes that
output image attributes.

Image Segmentation
• Segmentation procedures partition an image
into its constituent parts or objects.
• The more accurate the segmentation, the more
likely recognition is to succeed.
• It is generally used to locate objects and
boundaries in objects.

Representation and Description
• Representation: Make a decision whether the
data should be represented as a boundary or as
a complete region. It is almost always follows
the output of a segmentation stage.
• Boundary Representation: Focus on external
shape characteristics, such as corners and
Inflections.
• Region Representation: Focus on internal
properties, such as texture or skeleton Shape.

Representation and Description
• Choosing a representation is only part of the solution for
transforming raw data into a form suitable for subsequent
computer processing (mainly recognition).
• Description: also called, feature selection, deals with
extracting attributes that result in some information of interest.

Object Recognition
• Recognition: The process that assigns label to
an object based on the information provided by
its description.

Knowledge Base
• Knowledge about a problem domain is coded
into an image processing system in the form of
a knowledge database.

Spatial and Intensity Resolution
• Spatial resolution is a measure of the smallest
discernible details in an image.
• It can also be stated in number of ways, dots (pixels)
per unit distance or line pairs per unit distance.
• Measuring spatial resolution
1. Dots per inch
2. Lines per inch
3. Pixels per inch

Measuring Spatial Resolution
1. Dots per inch
• DPI is a measure of image resolution.
• DPI means that how many dots of ink are printed per inch.
When an image get printed out from printer.
• The higher the DPI of the printer the higher is the quality of
the printed image or paper.
• Commonly used in printing and publishing industry.
• For Newspapers: 75dpi, Magazines: 133dpi, Glossy
Brochure:175dpi and Book pages: 2400 dpi.

2. Lines per inch
• LPI is used to measure the resolution of images printed in
halftones (An image comprised of such dots of one color is
usually called a halftone image). Because halftone images are
printed as a series of dots.
• LPI is a measurement of printing resolution.
• LPI is usually used in laser printers and graphic design.

3. Pixels per inch
• PPI refers display resolution, or how many
individual pixels are displayed in one inch of a digital image.
• PPI is measure for different devices such as tablets, mobile
phones etc.
• The higher is the PPI, the higher is the quality.

Intensity Resolution
• Intensity resolution refers to the smallest
discernible change in intensity level.
• Intensity of resolution means the number of
pixels per square inch, which determines the
clarity or sharpness of an image.

Illustration of the effects of reducing image spatial resolution

Effects of varying the number of intensity levels in a digital image
Here Value of K varies from 8 to 1(8,7,6,5,4,3,2,1)

Image Interpolation
• Interpolation is a tool which is used to resize the
image such as zooming, shrinking, rotating and
geometric corrections.
• Image Interpolation is also called as re-sampling of
image.
• In order to resize the image, we have to resample the
image.
• For Example: Suppose that an image of size 500 ×
500 pixels has to be enlarged 1.5 times to 750 × 750
pixels.

Nearest Neighbor Interpolation
• In this method, it assigns to each new location the
intensity of its nearest neighbor in the original image.
• Bilinear interpolation: In which we use the four
nearest neighbors to estimate the intensity at a given
location.
• Bicubic interpolation: which involves the sixteen
nearest neighbors of a point.

Introduction to Color Image Representation
• Color is a powerful descriptor which simplifies object
identification and extraction from a scene.
• Human being can perceive far more higher number of color
shades than gray scale shades.
• Color Image Processing is divided into two major categories:
1. Full color: The images are acquired with a full-color sensor,
such as a color TV or color scanner.
2. Pseudo-color: Assign a color to a particular range of
intensities.
Pseudo color images are grayscale which are assigned color
based on the intensities values.

Full color & Pseudo-color Processing

Characterization of light
• If the light is Achromatic, its only attribute is
its intensity. Achromatic light is what viewers
see on a black and white television set.
• Chromatic light spans the electromagnetic
spectrum from 400 to 700nm.
• Three quantities
chromatic light:
brightness.
describe the
radiance, luminance
quality of
and

Characterization of light
• Radiance (Watts-W): It is the total amount of
energy coming out of the light source.
• Luminance (lumens-lm):It gives a measure of
amount of energy an observer perceives from a
light source.
• Brightness (no unit): It is subjective measure
that is practically impossible to measure. It
corresponds to achromatic attribute of
intensity.

Color Standardization
• CIE (Commission International Eclairage)
wavelength values to
has
three
standardized specific
primary colors:
• Blue= 435.8nm
• Green= 546.1nm
• Red=700nm

Primary & Secondary Colors
• The primary colors can be added to produce the secondary
colors of light-
o Magenta (red plus blue)
o Cyan (green plus blue)
o Yellow (red plus green)
• Mixing the three primaries or secondary with its opposite
primary color, in the right intensities produces white light.

Primary & Secondary Colors
• The primary colors of pigments are magenta, cyan and
yellow and the secondary colors are red, green and blue.
• A proper combination of the three pigment primaries or a
secondary with its opposite primary produces black.

Characterizing Color
• One color can be distinguished from other by using
three characteristics.
• Brightness- It embodies the achromatic notion of
intensity.
• Hue- Dominant color (wavelength of light) as
perceived by an observer. It is measure of color of the
object & its hue is expressed as an angle.
• Saturation- It refers to the relative purity or the
amount of white light mixed with a hue. Or It is a
measure of the richness of color.

Assignment Questions
Q1 Define the image and digital image processing.
Explain the fundamental steps of digital image
processing with suitable diagram.
Q2 What are the applications of image processing?
Explain components of image processing system.
Q3 Explain image sensing & acquisition.
Q4 Explain image sampling and Quantization process in
image processing?
Q5 Explain digital image representation.

DIP Notes Unit-1 PPT , engineering, computer Science

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DIP Notes Unit-1 PPT , engineering, computer Science