Not currently offered

Credit points: 15

Subject outline

This subject will cover the fundamentals of discrete signal and systems, basic image processing techniques in spatial domain and transform domain, and advanced topics such as wavelet-based image processing and compression, and edge-aware filtering. After completing this subject students will have advanced and integrated understanding of these topics and skills in some state-of-art techniques. Students will be able to research or apply established theories to practical problems such as image enhancement and feature extraction.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorDennis Deng

Available to Study Abroad StudentsNo

Subject year levelYear Level 5 - Masters

Exchange StudentsNo

Subject particulars

Subject rules

Prerequisites Students must be admitted in one of the following courses: SMENE, SMENEB, SMELE, SMTNE or LMEM


Incompatible subjectsN/A

Equivalent subjectsN/A

Special conditionsN/A


Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsDigital Image Processing using MATLABRecommendedR. C. Gonzalez, R. E. Woods, S. L. Eddins, 2009Pearson-Prentice-Hall
ReadingsComputer Vision: Algorithms and ApplicationsRecommendedR. Szeliski, 2011Springer
ReadingsIntroduction to digital image processingRecommendedW.K. Pratt, 2013CRC press
ReadingsDigital signal processing A computer-based approachRecommendedS. Mitra, 2006McGraw Hill

Graduate capabilities & intended learning outcomes

01. Demonstrate deep understanding of the fundamental principles in digital signal processing

Lecture, assignments

02. Demonstrate specialised and in-depth knowledge in image processing by applying knowledge to practical applications.

Lecture, laboratory class

03. Produced specialised MATLAB functions for advanced image processing building on existing algorithms and further developing new algorithms

Laboratory class

04. Critically analyse, evaluate and synthesise fundamental concepts and recent advances in image processing for application to complex problems

Lecture and assignments

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