College of Science

Courses

Digital Image Processing

Course Code C0172 Th 4 Pr 4 CrHrs 6

This course is an introduction to the fundamental concepts and techniques in basic digital image processing and their applications to solve real life problems.  The topics covered include Digital Image Fundamentals, Image Transforms, Image Enhancement, Restoration and Compression, Morphological Image Processing, Nonlinear Image Processing, and Image Analysis. Application examples are also included

  1. Demonstrated understanding of the basic concepts of two-dimensional signal acquisition, sampling, and quantization.
  2. Demonstrated understanding of spatial filtering techniques, including linear and nonlinear methods.
  3. Demonstrated understanding of 2D Fourier transform concepts, including the 2D DFT and FFT, and their use in frequency domain filtering.
  4. Demonstrated understanding of the Human Visual System (HVS) and its effect on image perception and understanding.
  5. Demonstrated understanding of the fundamental image enhancement algorithms such as histogram modification, contrast manipulation, and edge detection.
  6. Demonstrated programming skills in digital image processing related problems.

Distribution of Marks

Final Mark

Final Exam

Second Term

Mid-Year

First Term

100

Prac.

Theor.

Prac.

Theor.

Prac.

Theor.

Prac.

Theor.

20

30

5

5

10

20

5

5

References

SN

Gonzalez, R. C., & Woods, R. E. (2007). Digital image processing 3rd edition.

1

Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2004). Digital image processing using MATLAB. Pearson Education India.

2

Sonka, M., Hlavac, V., & Boyle, R. (2014). Image processing, analysis, and machine vision. Cengage Learning.

3

Digital Image Processing – Introduction

Example of Digital Image Processing

Example of Operations in Digital Image Processing

Key Stages in Digital Image Processing

Structure of Image

Types of processing on Digital Images

Image Arithmetic: Adding & Subtracting

Multiplying & Dividing Images

Histograms & Dynamic Range

Invers, Threshold, Log & exp Transformations

Contrast Enhancement: Auto Adjustment Contrast

Histogram Equations

Piecewise Histogram

Gamma Correction & Alpha Blending

Image Resizing: Nearest Neighbor

Mid-term exams

Image Resizing: Bilinear Interpolation

Spatial & Frequency Domains

Spatial Filter, Convolution & Correlations

Smoothing, Averaging & Blurred Filter

Noise Reduce: Median, Weighted Median

Gaussian filter 

Sharping: Laplacian ¼ & 1/8

Edge Detection: Sobel

Robert & Prewitt

Color Image Type Introduction

 Indexed Organization

Scalar Color Quantization

Gray Level, RGB

HSV & HLS convert between them

Frequency Domain Introduction

Fourier Transform

Examples