Image Processing - PH618

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Module delivery information

This module is not currently running in 2020 to 2021.


Introduction to Matlab
• Image representation,
• Image formation,
• Grey-scale transformation,
• Enhancement and extraction of image content,
• Fourier transforms and the frequency domain,
• Image restoration, geometrical transformations,
• Morphology and morphological transformations,
• Feature extraction,
• Segmentation.


This module appears in the following module collections.

Contact hours

Lectures (18 hours), console sessions (12 hours).
This module is expected to occupy 150 total study hours, including directed reading, console/computer-based exercises and mathematical and conceptual problem-solving.


This is not available as a wild module.

Method of assessment

Coursework 40% including class tests;
Final exam 60%.

Indicative reading

Gonzalez and Woods, Digital Image Processing, Addison-Wesley, 1992, ISBN 0-201-50803-6

  • Fundamentals of digital image processing: a practical approach with examples in Matlab, Solomon, Chris, Breckon, Toby 2011
  • John C. Russ, The Image Processing Handbook, CRC Press, 1995
  • D. Hanselman and B. Littlefield, Mastering Matlab 7, Prentice-Hall, 2005, ISBN 0-13-243767-8

    See the library reading list for this module (Canterbury)

    Learning outcomes

    Knowledge and understanding of laws and principles of imaging processing, and their application to diverse areas of physics.

  • An ability to solve problems in image processing using appropriate mathematical tools.
  • Competent use of appropriate C&IT packages/systems for the analysis of images and the retrieval of appropriate information.
  • An ability to present, process and interpret information graphically.
  • An ability to make use of appropriate texts, research-based materials or other learning resources as part of managing their own learning.
  • Problem-solving skills, in the context of both problems with well-defined solutions and open-ended problems; an ability to formulate problems in precise terms and to identify key issues, and the confidence to try different approaches in order to make progress on challenging problems. Numeracy is subsumed within this area.
  • Analytical skills – associated with the need to pay attention to detail and to develop an ability to manipulate precise and intricate ideas, to construct logical arguments and to use technical language correctly.


    1. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
    2. The named convenor is the convenor for the current academic session.
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