MSc in Electrical Engineering

Course Information Package

Course Unit TitleDIGITAL IMAGE PROCESSING
Course Unit CodeAEEE514
Course Unit DetailsMSc Electrical Engineering (Technical Electives) -
Number of ECTS credits allocated7
Learning Outcomes of the course unitBy the end of the course, the students should be able to:
  1. Describe the basic components of a general-purpose image processing system and explain the fundamental steps in digital image processing.
  2. Setup the image processing problem and decide how the various image enhancement techniques in the spatial and frequency domains will improve the image.
  3. Evaluate a given image and determine the appropriate mask for a specific enhancement requirement.
  4. Design and use in smoothing filters, Laplacian filters and high boost filters.
  5. Formulate and analyze high order statistics filters, median filters, and max-min filters.
  6. Describe the use of edge detection techniques and image interpolation and evaluate the results of the various image enhancement techniques.
  7. Distinguish and apply various image restoration techniques and evaluate their results.
  8. Use image processing MATLAB software tools for solving image processing problems.
  9. Study an advanced application topic of Image processing.
Mode of DeliveryFace-to-face
PrerequisitesNONECo-requisitesNONE
Recommended optional program componentsNONE
Course Contents

      Applications of image processing.

      Formation of digital images. Image sensing, acquisition sampling and quantization. Spatial and Gray level resolution. Encoding.

      Image enhancement in the spatial domain. Gray level transformations. Histogram processing. Spatial filtering. Smoothing filters. Order statistic filters. Sharpening spatial filters. Laplacian . Combination of spatial enhancement methods.

      Image Enhancement in the frequency domain. Fourier transform and frequency domain. Two-dimensional Fourier Transform. DFT and its inverse. Smoothing frequency filters. Sharpening frequency domain filters. Homomorphic filters.

      Image Restoration. Modelling of the degradation-restoration process. Types of noise. Mean filters, order-statistics filters. Frequency domain filtering. Wiener and least squares filtering.

      Image Compression. Redundancy. Image compression models.

      Selected Advanced Topics. Morphological image processing, Image segmentation and object recognition.

MATLAB exercises.

Recommended and/or required reading:
Textbooks
  • R. Gonzales and R. Woods, Digital Image Processing, Prentice Hall, 2nd edition, 2002.
  • R. Gonzales and R. Woods, Digital Image Processing using MATLAB, Prentice Hall, 2004.
References
  • J. Russ, Introduction to Image processing and analysis, CRC press,2007.
  • W. Pratt, Digital Image Processing, 4th edition, J. Wiley, 2007
  • A. K. Jain, Fundamentals of Digital Image Processing, Prentice Hall, 1989.
Planned learning activities and teaching methodsThe teaching of the course is lecture-based (3 hours per week) in a classroom. A combination of traditional teaching with written notes on a white board and slide presentations using a projector for the presentation of the more complicated diagrams and graphs is utilized. Students are assessed continuously and their knowledge is checked through tests, assignments and the final exam. Lectures include the solution and discussion of example problems regarding the material presented. Relevant homework and assignments are given to the students for further study at their own. Due to the level and type of the course students are urged to participate in discussing the various topics and provide their opinion during problem-solving sessions. Lecture notes are compiled by students which serve to cover the main issues under consideration and serve as a guide for further reading. Students are also required to seriously use the textbook assigned to the course, in addition to other sources found either in the library or elsewhere in order to broaden their perspective on the various subjects presented in class and in the textbook. Additionally, students are expected to use the MATLAB Image Processing toolbox for the analysis of images and the application of the various image processing methods required in the homework assigned.
Assessment methods and criteria
Assignments20%
Tests30%
Final Exam50%
Language of instructionEnglish
Work placement(s)NO