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Parent Programme
Bachelor in Computing (Level 7 NFQ)
NFQ Level & Reference
Level 7 / Ref: M3.12
12 Weeks X 2.5 Hours per week
DSP & Image Processing (Elective)
Module Credit Units

DSP & Image Processing (Elective)

Introduction to DSP & Image Processing

The aim of this DSP & Image Processing module is to familiarise learners with concepts in digital signal processing and image processing and give the learners practical insights into current developments in the field. Learners should be able to apply these principles to specialist areas to solve bespoke data processing tasks and defend design decisions with well-articulated reports.

Indicative Syllabus Content

Digital signals

  • Continuous-time vs. discrete-time signals
  • Unit impulse and unit step functions
  • System response and convolution
  • Fourier Transform
  • Z-transform

Digital filters and filtering

  • Low-pass, high-pass, band-pass, and band-stop
  • Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters
  • Design specifications and filter characteristics
  • Windowing techniques for FIR filter design
  • Butterworth and Chebyshev filter design for IIR filters

Images and filtering

  • Digital image representation
  • Bit depth, channel and colour maps
  • Histogram equalisation
  • Smoothing and sharpening images
  • Convolution and correlation
  • Edge detection

Image transformations

  • Fourier transform in image processing
  • Discreet cosine transform
  • Radon transform
  • Hough transform

Technical Report writing

  • Identifying all underlying requirements for a software solution with a given specification
  • Flow chart design and re-evaluation process
  • Report writing to address initial requirements with proposed solution

Applications of Digital signal processing and image processing

  • Speech and audio signal processing
  • Biomedical/physiological signal processing
  • Medical image processing
  • Feature identification in specialist domains

Minimum Intended Learning Outcomes (MIMLOs)

Upon successful completion of this module, the learner should be able to:
Discuss theory regarding digital signal processing and image processing.
Explain mathematical concepts behind digital signal processing and image processing.
Write documentation detailing a problem, solution needed, solution provided and an account of how proposed algorithms will meet these needs.
Apply digital signal processing and image processing to different domains of knowledge to solve problems.
Critically identify and evaluate key features in a dataset.


1, 2, 3, 4, 5
Continuous Assessment
Total 100%
Proctored Written Exam
All Assignments

Reassessment Opportunity

Where the combined marks of the assessment and examination do not reach the pass mark the learner will be required to repeat the element of assessment that they failed.   Reassessment materials will be published on Moodle after the Examination Board Meeting and will be aligned to the MIMLOs and learners will be capped at 40% unless there are personal mitigating circumstances.

Aims & Objectives

The DSP & Image Processing module will ensure learners meet the following objectives:

  • Core understanding of digital signal processing and image processing techniques.
  • Advanced mathematical representations of signals and images.
  • Familiarity with industry tools for signal and image processing to perform computational operations.
  • Applying material to real world datasets for complex problem solving.
  • Detailed report writing on algorithm design to present findings.