Unit-1Chapter-1Lecture-2.pptxkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk

DISCOVER . LEARN . EMPOWER
Apex Institute of Technology
Department of Computer Science & Engineering
Bachelor of Engineering (Computer Science & Engineering)
DIGITAL IMAGE PROCESSING– (20CST-481)
Prepared By: MR. Aadi Partap Singh (E15043)
1
2/28/2024 Unit1-Chapter-1_Lecture1.3_Image acquisition, sampling

DIGITAL IMAGE
PROCESSING
CO
Number
Title Level
CO1
To Understand the fundamental of digital
image processing with python.
Understand
CO2
To acquire the knowledge to apply various
image processing techniques and tools.
Understand
CO3
To learn the practical applications of
image processing steps to real world
problem.
Understand
Course Objective:
During the course, students will be able :
Will be covered in
this lecture
2

DIGITAL IMAGE
PROCESSING
CO
Number
Title Level
CO1
Fundaments and techniques implemented in
digital image processing technologies
Understand
CO2
Understanding the various filters applications,
smoothing applications and techniques by
image processing implementation by python
Understand
CO3
Acquiring knowledge on various compression
and segmentation techniques, for image
enhancement methods.
Understand
Course Outcome:
Upon successful completion of this course, students will be able to:
Will be covered in
this lecture
3

Recap of previous session
In previous session we tried understanding the below concept :
Need of Digital image processing
What is an Image?
What is digital image processing?
State of the art examples of digital image processing
What is pixel?
What is image resolution, and its importance
2/28/2024 Unit1-Chapter-1_Lecture1.3_Image acquisition, sampling 4

Unit-1: Fundamentals of Image processing
Chapter-1: Introduction to Image Processing
Lecture: 3 Image acquisition , sampling and
quantization
5
Welcome to the session of

CONTENTS
This presentation covers:
 Image Acquisition and Representation
 A Simple Image Formation Model
 Image Sampling and Quantization
 Image Interpolation
Link: https://medium.com/futframe-ai/fundamental-steps-of-digital-image-processing-d7518d6bb23c
6

Acquisition
7
* Reference : R.Gonzalez and R.Woods, “Digital Image Processing – 2 n
d
Edition”, Prentice Hall, 2 0 0 2

A Simple Image Formation Model
0 < f (x, y) < 
f (x, y) i(x, y) r(x, y)
where
0 < i(x, y) < 
and
0 < r(x, y) < 1
f (x, y) : intensity at the point (x, y)
i(x, y) : illumination at the point (x, y)
(the amount of source illumination incident on the scene)
r(x, y) : reflectance/transmissivity at the point (x, y)
(the amount of illumination reflected/transmitted by the object)
8
* Reference: Digital Image Processing - Algorithms and Applications by I. Pitas, Publisher: John Wiley.

Some Typical Ranges of illumination
Illumination
Lumen — A unit of light flow or luminous flux
Lumen per square meter (lm/m2) — The metric unit of measure for illuminance of a surface
• On a clear day, the sun may produce in excess of 90,000 lm/m2 of illumination on the surface of the
Earth
• On a cloudy day, the sun may produce less than 10,000 lm/m2 of illumination on the surface of the
Earth
• On a clear evening, the moon yields about 0.1 lm/m2 of illumination
• The typical illumination level in a commercial office is about 1000 lm/m2
9

Some Typical Ranges of Reflectance
Reflectance
• 0.01 for black velvet
• 0.65 for stainless steel
• 0.80 for flat-white wall paint
• 0.90 for silver-plated metal
• 0.93 for snow
10

Digital vs. Analog Images
Analog:
Function v = f(x,y): v,x,y are REAL
Digital:
Function v = f(x,y): v,x,y are INTEGER
11

Sampling means measuring the value of an image at a
finite number of points.
Quantization is the representation of the measured value
at the sampled point by an integer.
12

13
d

Stepping down from REALity to INTEGER coordinates x,y:
Sampling
14
d

Sampling and Quantization
15
d

Image quantization(example)
256 gray levels (8bits/pixel) 32 gray levels (5 bits/pixel) 16 gray levels (4 bits/pixel)
8 gray levels (3 bits/pixel) 4 gray levels (2 bits/pixel) 2 gray levels (1 bit/pixel)
16
d

Image sampling (example)
original image sampled by a factor of 2
sampled by a factor of 4 sampled by a factor of 8
17
d

Image downsampling by factor of 2
18
d

Factor of 2 Up-Sampling
19
d

Color images can be represented by 3D Arrays (e.g. 320 x 240 x 3)
20
d

But for the time being we’ll handle 2D grayvalue images
21
d

Image Interpolation
Interpolation — Process of using known data to estimate
unknown values
e.g., zooming, shrinking, rotating, and geometric correction
Interpolation (sometimes called resampling) — an imaging method to
increase (or decrease) the number of pixels in a digital image.
Some digital cameras use interpolation to produce a larger image than the
sensor captured or to create digital zoom
22

23
d

Image Interpolation:
Nearest Neighbor Interpolation
f1(x2,y2) =
f(round(x2), round(y2))
=f(x1,y1)
f(x1,y1)
f1(x3,y3) =
f(round(x3), round(y3))
=f(x1,y1)
24
d

Image Interpolation: Bilinear Interpolation
(x,y)
25
The output pixel value is a weighted average of pixels in the nearest 2-by-2 neighborhood
Considers the closest 2x2 neighborhood of known pixel values surrounding the unknown pixel
It then takes a weighted average of these 4 pixels to arrive at its final interpolated value
This results in much smoother looking images than nearest neighbor
d

26
Image Interpolation: Bilinear Interpolation
f(x,y)=ax + by + cxy + d

3 3
3
i j
ij
a x y
f (x, y)  
Image Interpolation:
Bicubic Interpolation
The intensity value assigned to point (x,y) is obtained by the
following equation
i0 j0
The sixteen coefficients are determined by using the sixteen
nearest neighbors.
27

Homework
Consider the following 4x4 image. Construct the 8x8 image using nearest neighbor
and bilinear interpolation techniques.
9 8 7 6
8 8 4 6
1 1 4 6
0 9 2 3
28

Summary
In todays session we tried understanding the below concept :
Image acquisition
Image sampling
Image quantization
Image interpolation
29

References:
 https://www.simplilearn.com/how-facebook-is-using-big-data-article?source=CTAexp
 https://www.icas.com/ca-today-news/10-companies-using-big-data
 https://www.bernardmarr.com/default.asp?contentID=1076
 Bryant, R.E., Katz, R.H., Lazowska, E.D.: Big-Data Computing: Creating Revolutionary Breakthroughs in
Commerce, Science and Society
 Sathi, A.: Implementation section (book 1). In: Big Data Analytics: Disruptive Technologies for Changing the
Game, 1st ed. MC Press Online (2012)
 R. Gonzalez and R. Woods, “Digital Image Processing – 2 n
d
Edition”, Prentice Hall, 2002
30

Assessment Pattern
S.No. Item Number/semester Marks
1 MSTs 2 20 per each
2 Quiz 2 per unit 4 per each quiz
3
Time bound surprise
test
3 (one per unit) 12 per each test
4 Assignments 3 (one per unit) 10 per each Assignment
5
Engagement task (non
gradable)
One per each topic depends
6
Attendance +
Engagement score
Above 90% 2
Internal (division as mentioned above points 1-6) 40
External 60
Total 100
31

THANK YOU
For queries
Email: aadi.e15043@cumail.in
32

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