Image Processing

GANPAT UNIVERSITY 

FACULTY OF ENGINEERING & TECHNOLOGY

Programme

Bachelor of Technology

Branch/Spec.

Computer Science & Engineering (CBA/BDA/CS)

Semester

VI

Version

1.1.0.1

Effective from Academic Year

2021-22

Effective for the batch Admitted in

June 2019

Subject code

2CSE60E20

Subject Name

Image Processing

Teaching scheme 

Examination scheme (Marks)

(Per week)

Lecture (DT)

Practical (Lab.)

Total

CE

SEE

Total

L

TU

P

TW

Credit 

2

0

1

0

3

Theory 

40

60

100

Hours

2

0

2

0

4

Practical

30

20

50

Pre-requisites:

Engineering Mathematics, Algorithms

Objectives of the Course:

• Understand image formation and the role human visual system plays in perception of grey and colour image data. 
• Describe various applications of image processing in various sectors like medical, defence, etc. 
• Learn the signal processing algorithms and techniques in image enhancement and image restoration.
• Apply various image processing techniques to solve real world problems.

Theory Syllabus

Unit

Content

Hrs

1.

Introduction and Digital Image Fundamentals:

Digital Image Fundamentals, Human visual system, Image as a 2D data, Image representation – Gray scale and Color images, image sampling and quantization

3

2.

Image enhancement in Spatial domain:

Basic gray level Transformations, Histogram Processing Techniques,  Histogram equalization, Histogram Matching, Spatial Filtering, Low pass filtering, High pass filtering, Mexican Hat Transformation, 

5

3.

Filtering in the Frequency Domain:

Introduction to the Fourier transform and frequency domain concepts, Extension to functions of two variables, low pass filter, high pass filter, Laplace transformation, Image Smoothing, Image Sharpening, Homo-morphic filtering

5

4.

Image Restoration and Reconstruction:

Various noise models, image restoration using spatial domain filtering, image restoration using frequency domain filtering, Estimating the degradation function, Inverse filtering.

5

5.

Colour Image Processing:

Colour Fundamentals, Colour Models, Pseudo colour image processing

4

6.

Image Compression:

Fundamentals of redundancies, Basic Compression Methods: Huffman coding, Arithmetic coding, Error free compression, Lossy compression. LZW coding, JPEG Compression standard

4

7.

Morphological Image Processing:

Erosion, dilation, opening, closing, Basic Morphological Algorithms: hole filling, connected components, thinning, skeletons

4

Self-Learning

Fundamentals of redundancies, Basic Compression Methods: Huffman coding, Arithmetic coding, Error free compression, Lossy compression. LZW coding, JPEG Compression standard

Practical Contents

• Read an 8 bit image and then apply different image enhancement techniques: (a) Brightness improvement (b) Brightness reduction (c) Thresholding (d) Negative of an image (e) Log transformation (f) Power Law transformation. 
• Implement different interpolation techniques using MATLAB/ Scilab 
• Read an image, plot its histogram then do histogram equalization. Comment about the result.      
• Implement various Smoothing spatial filters. 
• Write a program to implement various low pass filters and high pass filters in frequency domain. 
• Write a program for erosion and dilation, opening & closing using inbuilt and without inbuilt function. 
• Implement and study the effect of Different Mask (Sobel, Prewitt and Roberts)
• Implement various noise models and their Histogram 
• Implement inverse filter and wiener filter over image and comment on them 
• Implement Image compression using DCT Transform                 

Text Books

1.

Digital Image Processing, 3rd Edition, by Rafael C Gonzalez and Richard E Woods. Publisher: Pearson Education

Reference Books

1

Milan Sonka, Vaclav Hlavav, Roger Boyle, ―Image Processing, Analysis and Machine Vision‖, 2nd ed., Thomson Learning, 2001

2

Pratt W.K, ―Digital Image Processing‖, 3rd ed., John Wiley & Sons, 2007

3

Digital Image Processing Using Matlab, Rafel C. Gonzalez and Richard E. Woods, Pearson
Education

4

Fundamentals of Digital Image Processing by Anil K Jain, PHI

Course Outcomes:

Cos

Description

CO1

Understand image formation and the role human visual system plays in perception of gray and colour image data.

CO2

Describe various applications of image processing in various sectors like medical, defence, etc.

CO3

Learn the signal processing algorithms and techniques in image enhancement and image restoration.

CO4

Apply various image processing techniques to solve real world problems.

Mapping of CO and PO: