Computational Imaging Methods (G6087)
Computational Imaging Methods
Module G6087
Module details for 2025/26.
15 credits
FHEQ Level 6
Module Outline
This module will develop your knowledge and understanding of recent methodological developments for image analysis and reconstruction. We will describe a variety of common use-cases and discuss limitations of current approaches and open challenges.
Key topics include:
• Principles and methods for inference in computational models of imaging data.
• Approaches for standard computer vision tasks such as segmentation, detection, and tracking.
• Generative models and their application for tasks in image synthesis and analysis.
• 3D image reconstruction for photographic and medical imaging.
A range of relevant machine learning and statistical analysis techniques will be introduced as we discuss each of these topics. You will be exposed to a range of applications across photographic and biomedical imaging domains and will learn how to develop and critique potential solutions for different problems.
This module has prerequisite requirements of prior training in fundamentals of machine learning or statistical modelling, relevant mathematics (linear algebra, probability, optimisation) and programming in a suitable language.
Module learning outcomes
Demonstrate systematic understanding of the key methodological principles in image analysis and reconstruction
Demonstrate critical awareness of limitations and challenges when applying an analysis approach to a specified problem or dataset
Propose methodological solutions, based on recent research, for a specified imaging problem
Critically evaluate an implemented system
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 100.00% | |
| Coursework components. Weighted as shown below. | ||
| Report | A1 Week 1 | 100.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Autumn Semester | Lecture | 1 hour | 11111111111 |
| Autumn Semester | Seminar | 1 hour | 11111111111 |
| Autumn Semester | Laboratory | 2 hours | 11111111111 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
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