This document provides information on an image processing course offered at the University. The 3 credit, semester long course is open to students in computer science, information science, electronics, and electrical engineering. The course introduces fundamental concepts of digital image processing including image formation, representation, and basic operations. It covers topics like image transformation, filtering, restoration, and compression. The course aims to familiarize students with principles and applications of image processing to help with final year projects.

1. Course Title: Image processing COURSE CODE:UEC635N
CREDITS: 3 (3-0-0) Teaching Hours: 40 Hrs
(10 Hrs/Unit)
L-T-P: 3-0-0
CIE Marks: 50 SEE Marks: 50 Total Marks: 100
COURSE TYPE: OPEN ELECTIVE SEMESTER: B.E VI sem
Department: Electronics and Communication Engineering
COURSE PRE-REQUISITES: Linear Algebra, Differential Equations, Probability and Statistics,
Calculus, Signals and systems,Digital Electronics (just basic) and Basic Programming skills (MATLAB or
any).
ELIGIBILITY: CSE, ISE, EIE,EEE
About Image Processing:
Image processing is a method to perform some operations on an image, in order to get an enhanced
image or to extract some useful information from it. It is a type of signal processing in which input
is an image and output may be image or characteristics/features associated with that image.
Nowadays, image processing is among rapidly growing technologies. It forms core research area
within engineering and computer science disciplines too. Its helps for students to implement their
final year projects.
COURSE OBJECTIVES:
1. To familiarize the basic principles of digital image processing and its components.
2. To understand the process of image transformation, histogram processing and spatial filter
application.
3. To understand the significance of image restoration and colour image processing.
4. To understand the needs of image compression and various image compression techniques.
COURSE OUTCOMES:
A student who successfully completes this course should be able to
1. Articulate the fundamentals of Digital image processing including the simple image formation
and relationship between pixels
2. Application of different types of Image transformation techniques, histogram processing and
application of spatial filters.
3. Analyse the significance of image restoration and processing of colour images.
4. Illustrate the image compression like lossy and loss less image compression techniques.
The topics that enable to meet the above objectives and course outcomes are given below:
UNIT I (10 Hours)
Introduction to Image processing: Fundamental steps in image processing; Components of image
processing system; image sensing and acquisition; sampling and quantization; representation of

2. digital images, image interpolation, Basic relationship between pixels; arithmetic and logic
operations.
UNIT II (10 Hours)
Transformation and spatial filtering: Basics of intensity transformation and functions, Histogram
Processing, equalization and histogram matching.Basics of Spatial Filtering, Smoothing Spatial Filters,
Sharpening Spatial Filters. Image Restoration: Image Restoration: Image Degradation/Restoration Process,
Noise Models.
UNIT III (10 Hours)
Restoration in the Presence ofNoise Only-Spatial Filtering, Inverse Filtering, Minimum Mean Square Error
(Wiener) Filtering,Color image processing: fundamentals, color models pseudo colour image processing,
colour transformations.
UNIT IV (10 Hours)
Image Compression: Fundamentals, Image Compression Models and methods: Huffman coding, Golomb
coding, arithmetic coding, LZW coding JPEG, predictive coding. Digital watermarking Applications in
satellite, sonar, radar, medical areas and process industries.
Text Books:
1. R. C. Gonzalez, R. E. Woods, “Digital Image processing”, Addison Wesley/ Pearson education,
New Delhi, India, 3rd edition, 2002.
Reference Books:
1. A. K. Jain, “Fundamentals of Digital Image processing”, Prentice Hall of India, New Delhi, 2nd Edition,
1997.
2. Rafael C. Gonzalez, “Digital Image processing using MATLAB”,Richard E. Woods and Steven Low
price Edition, Pearson Education Asia, India, 2nd Edition, 2004.
3. William K. Pratt,“Digital Image Processing”,John Wiley & Sons, New Delhi, India, 3 rd edition, 2004.
4. Arthur R. Weeks, Jr, “Fundamentals of Electronic Image Processing”,SPIE Optical Engineering Press,
New Delhi, India, 2nd Edition, 1996.
5. S.Jayaraman,s.Esakkirajan,T.Veerakumar,“Digital ImageProcessing”,Tata McGraw-Hill Education.
CO-PO AND CO-PSO MAPPING
Course Outcomes
CO- PO, PSO Mapping
(3/2/1 indicates strength of correlation) 3-Strong, 2-
Medium, 1-Weak
POs PSOs
a b c d e f g h i j k l m n o
CO 1:Understand the fundamentals of
Digital image processing including the
simple image formation and relationship
between pixels.
2 3 3 3 3
CO2 :Understand the different types of
Image enhancement techniques in
spatial and frequency domain.
1 3 2 3 2 2 2

3. CO3 :Understand the different types of
image degradation like linear image
restoration techniques and nonlinear
image restoration techniques.
1 3 2 3 1 3
CO4 :Understand the image
compression like lossy and loss less
image compression techniques and also
understand the need of image
segmentation.
1 3 3 3 3 3 3 3
WEB SOURCE REFERENCES:
1 http://www.cse.iitd.ernet.in%2F~sak%2Fcourses%2Fcdp%2Fslides.pdf&ei=xl4xUsWwI8n_rQe24YD
oAQ&usg=AFQjCNFeZnxu6BwhgXtl0FMEDQFq9FECzw&bvm=bv.52109249,d.bmk
2 http://nptel.iitm.ac.in/courses/Webcourse-contents/IIT-KANPUR/compiler-desing/
3 http://www.diku.dk/~torbenm/Basics/basics_lulu2.pdf
4 http://www.antlr.org/wiki/display/ANTLR3/Tutorials
5 http://javacc.java.net/
6 http://www.engr.mun.ca/~theo/JavaCC-Tutorial/javacc-tutorial.pdf
POs from NBA
1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering
fundamentals, and an engineering specialization to the solution of complex engineering problems.
2. Problem analysis: Identify, formulate, review research literature, and analyze complex
engineering problems reaching substantiated conclusions using first principles of mathematics,
natural sciences, and engineering sciences.
3. Design/development of solutions: Design solutions for complex engineering problems and
design system components or processes that meet the specified needs with appropriate consideration
for the public health and safety, and the cultural, societal, and environmental considerations.
4. Conduct investigations of complex problems: Use research-based knowledge and research
methods including design of experiments, analysis and interpretation of data, and synthesis of the
information to provide valid conclusions.
5. Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern
engineering and IT tools including prediction and modeling to complex engineering activities with
an understanding of the limitations.
6. The engineer and society: Apply reasoning informed by the contextual knowledge to assess
societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the
professional engineering practice.

4. 7. Environment and sustainability: Understand the impact of the professional engineering
solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for
sustainable development.
8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms
of the engineering practice.
9. Individual and team work: Function effectively as an individual, and as a member or leader in
diverse teams, and in multidisciplinary settings.
10. Communication: Communicate effectively on complex engineering activities with the
engineering community and with society at large, such as, being able to comprehend and write
effective reports and design documentation, make effective presentations, and give and receive clear
instructions.
11. Project management and finance: Demonstrate knowledge and understanding of the
engineering and management principles and apply these to one’s own work, as a member and leader
in a team, to manage projects and in multidisciplinary environments.
12. Life-long learning: Recognize the need for, and have the preparation and ability to engage in
independent and life-long learning in the broadest context of technological change.
Program Specific Outcomes
1.Analyse and design systems for electronics, Communication, and Signal Processing Applications.
2. Use domain specific tools for design, analysis, synthesis, and Validation of VLSI and embedded
systems.
3. Demonstrate the conceptual knowledge with respect to architecture, design, analysis, and
simulation of computer networking and applications.
Prepared by
Dr. Vijaylakshmi Jigajinni