Muhammad Shahid Farid

Course Overview

The course of CS-573 – Digital Image Processing is particularly designed to introduce students to the concepts, tools, and techniques of image processing. This course is designed as a graduate-level elective for M.Phil. in Computer Science. It teaches students the foundations as well as emerging trends in the fields of image processing, including: visual perception, image acquisition, representation, spatial transformations, frequency domain image processing, image enhancement, color image representation and processing, edge detection, image compression, image segmentation, and morphological image processing.

Course Outline

1. Lecture 01Introduction to Image Processing and its applications in various fields
2. Lecture 02Human visual perception, Light and electromagnetic spectrum, Image acquisition, Sampling and Quantization
3. Lecture 03Image Sensing and Acquisition, Image Sampling and Qunatization
4. Lecture 04Raster versus vector images, Progressive versus interlaced display, Popular image file formats, Why so many formats?, Basic Relationships Between Pixels
5. Lecture 05Point wise operations, Contrast Stretching, Bit-Plane Slicing
6. Lecture 06Histogram Processing, Histogram equalization, Histogram matching
7. Lecture 07Local Histogram processing, Enhancement using histogram statistics, Enhancement Using Arithmetic/Logic Operations
8. Lecture 08Image filtering in spatial domain, Smoothing filters, Order statistics filter, Sharpening filters
9. Lecture 09-11Fourier Transform and its applications in image processing
10. Lecture 12Properties of Discrete Fourier Transform (DFT)
11. Lecture 13Fileting in frequency domain, smoothing and sharping revisited
12. Lecture 14Selective Filtering, Homomorphic Filtering, Notch Filter
13. Lecture 15Introduction to image restoration and different noise models
14. Lecture 16Image restoration filters, Periodic Noise
15. Lecture 17Color models, Color transformations, Color image processing
16. Lecture 18Morphological Image Processing: Some Basic Concepts from Set Theory, Dilation and Erosion, Opening and Closing
17. Lecture 19The Hit-or-Miss Transformation, Some Basic Morphological Algorithms, Some Applications of Gray-Scale Morphology
18. Lecture 20Fundamentals of image segmentation: Point, Line, and Edge Detection
19. Lecture 21Boundary Detection, Thresholding, Region-Based Segmentation
20. Lecture 22Segmentation by Morphological Watersheds, Motion-based Segmentation
21. Lecture 23Texture Synthesis
22. Lecture 24Image Inpainting
23. Lecture 25Content-based Image Retrieval
24. Lecture 26Wavelets and Multiresolution Processing: Image Pyramids, Subband Coding, The Haar Transform
25. Lecture 27Multiresolution Expansions, Wavelet Transforms in One Dimension
26. Lecture 28Image Qualaity Assessment
27. Lecture 29Introdcution to Various IQA Technqiues
28. Lecture 303D television technology: framework overview, display technologies
29. Lecture 313DV representation, compression, and quality assessment
30. Lecture 32Course Conclusions

Exam / Sessional Instruments

Text Books

• Rafael C. Gonzalez, Digital Image Processing. Pearson Education, 3rd Ed., 2009.
• Simon J.D. Prince, Computer Vision: Models, Learning, and Inference. Cambridge University Press, 2012.