This document provides a syllabus for a course on digital image processing, outlining 4 units that will be covered over 12 weeks. The units include topics like image enhancement, spatial and frequency domain filtering, image restoration, compression, and morphological operations. References for further reading on digital image processing are also provided.

1. Compiled by:
Dr. Samarth Borkar
Dept. of Electronics and Telecommunication Engg.
Goa College of Engineering
[2019]
www.samarthborkar.simplesite.com

2. UNIT - 1 (12 hours) SYLLABUS RC 2016-17
Elements of visual perception: Light and electromagnetic spectrum, image sensing and acquisition, image
sampling and quantization, basic relationship between pixels: neighbors, connectivity, distance measures,
and arithmetic & logic operations.
Image enhancement: Translation, Rotation, Concatenation.
Basic transformations: Image negatives, Log Power-law, Contrast-stretching, Intensitylevel slicing, Bit-plane
slicing.
Histogram processing: Histogram equalization, Histogram equalization and Perspective Transformation.
UNIT - 2 (12 hours)
Spatial filtering: Smoothing, sharpening filters: Laplacian filters, Unsharp masking and highboost filtering.
Introduction to Fourier Transform and DFT: Properties of 2D Fourier Transform, FFT, Separable Image
Transforms, Walsh, Hadamard, Discrete Cosine Transform, Haar, Slant – Karhunen – Loeve transforms.
Frequency domain filters: (High pass and low pass) Ideal, Butterworth, Gaussian; Homomorphic filtering.
UNIT - 3 (12 hours)
Image Restoration: Image degradation/restoration process, noise models, restoration in the presence of
noise only- spatial filtering, periodic noise reduction by frequency domain filtering. Linear position invariant
degradations, estimating the degradation function, Inverse filter, Wiener filter.
Image Compression: Lossless and Lossy Coding, Block Transform Coding, predictive coding, JPEG, MPEG.
UNIT - 4 (12 hours)
Edge detection: Detection of point, line, discontinuities. Gradient Operators, Laplacian, LoG Filters, Global
Processing via Hough Transform.
Mathematical morphology: Binary Morphology, Dilation, Erosion, Opening and Closing, Duality Relations,
Gray Scale Morphology, Hit-and-Miss Transform, Thinning and Shape Decomposition.
Computer Tomography: Radon transform, Back-Projection Operator, Fourier-slice theorem, CBP and FBP
methods.

3. Recommended Readings:
1. Rafael C. Gonzalez and Richard E. Woods; Digital Image Processing; 3rd Ed. Pearson
2. Anil K. Jain; Fundamentals of Digital Image Processing; Prentice Hall of India
3. W. K. Pratt; Digital image processing, Prentice Hall.
4. Sonka, Hlavac, Boyle; Image Processing: Analysis and Machine Vision; Thomson

4. Useful Books on DIP

5. The first photograph was
taken in 1814 by a French
inventor Joseph Nicephore
Niepce.
The first underwater
photograph was taken by an
English mathematician William
Thomson using a water tight
box. This was done in 1856.

6. Weeks 1 & 2 6

7. Weeks 1 & 2 7

8. Weeks 1 & 2 8

9. Weeks 1 & 2 9

11. Weeks 1 & 2 11

12. Weeks 1 & 2 12

13. Weeks 1 & 2 13

14. Q1. What is meant by Digital Image Processing?
Explain how digital images can be represented.
►
►
►
►
►
►
Weeks 1 & 2 14

15. What is DIP? Contd..
►
Low Level Process
Input: Image
Output: Image
Examples: Noise
removal, image
sharpening, contrast
enhancement
Mid Level Process
Input: Image
Output: Attributes
Examples: Object
recognition,
segmentation,
classification based on
description
High Level Process
Input: Attributes
Output:
Understanding
Examples: Scene
understanding,
autonomous
navigation, cognitive.
In this subject (IP) we
shall stop here

16. Image Representation
Weeks 1 & 2 16

17. Weeks 1 & 2 17
Representing Digital Images
►The representation of an M×N numerical
array as
(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
 
  
 
 
    

18. Weeks 1 & 2 18
Representing Digital Images
►
0,0 0,1 0, 1
1,0 1,1 1, 1
1,0 1,1 1, 1
...
...
... ... ... ...
...
N
N
M M M N
a a a
a a a
A
a a a


   
 
 
 
 
 
 

19. Weeks 1 & 2 19
Representing Digital Images
►
(1,1) (1,2) ... (1, )
(2,1) (2,2) ... (2, )
( , )
... ... ... ...
( ,1) ( ,2) ... ( , )
f f f N
f f f N
f x y
f M f M f M N
 
 
 
 
 
 

20. Weeks 1 & 2 20
Representing Digital Images
►
►

21. Weeks 1 & 2 21


22. What is a Digital Image? (cont…)
►



►For most of this course we will focus on grey-scale
images

23. Weeks 1 & 2 23
Q2. What are the fundamental steps in
Digital Image Processing ?

24. Key Stages in Digital Image Processing
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Knowledge Base

25. Key Stages in Digital Image Processing:
Image Aquisition
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression

26. Key Stages in Digital Image Processing:
Image Enhancement
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression

27. Key Stages in Digital Image Processing:
Image Restoration
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression

28. Key Stages in Digital Image Processing:
Morphological Processing
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression

29. Key Stages in Digital Image Processing:
Segmentation
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression

30. Key Stages in Digital Image Processing:
Object Recognition
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression

31. Key Stages in Digital Image Processing:
Representation & Description
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression

32. Key Stages in Digital Image Processing:
Image Compression
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression

33. Key Stages in Digital Image Processing:
Colour Image Processing
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression

34. Q3. What are the components of an Image
Processing System?
Weeks 1 & 2 34

35. ►
►
►
►
►
►
Weeks 1 & 2 35

36. Weeks 1 & 2 36

37. Weeks 1 & 2 37

38. Weeks 1 & 2 38
Q4. Explain about elements of visual perception?

39. Weeks 1 & 2 39

40. Weeks 1 & 2 40

41. Weeks 1 & 2 41

42. Numerical
P2.1.
Weeks 1 & 2 42