COMP0114 Inverse Problems in Imaging

This database contains the 2018-19 versions of syllabuses.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

Academic session



Inverse Problems in Imaging



Module delivery

1819/A7U/T2/COMP0114 Masters (MEng)

Related deliveries

1819/A7P/T2/COMP0114 Postgraduate

Prior deliveries



Masters (MEng)

FHEQ Level


FHEQ credits



Term 2

Module leader

Arridge, Simon


Arridge, Simon

Module administrator

Ball, Louisa


To introduce the concepts of optimisation, and appropriate mathematical and numerical tools applications in image processing and image reconstruction.

Learning outcomes

On successful completion of the module, a student will be able to:

  1. To understand the principles of optimisation and to acquire skills in mathematical methods and programming techniques.

Availability and prerequisites

This module delivery is available for selection on the below-listed programmes. The relevant programme structure will specify whether the module is core, optional, or elective.

In order to be eligible to select this module as optional or elective, where available, students must meet all prerequisite conditions to the satisfaction of the module leader. Places for students taking the module as optional or elective are limited and will be allocated according to the department’s module selection policy.

Programmes on which available:

  • MEng Computer Science (International Programme) (Year 4)
  • MEng Computer Science (Year 4)
  • MEng Mathematical Computation (International Programme) (Year 4)
  • MEng Mathematical Computation (Year 4)


Students should have taken the term 1 module Machine Vision (COMP0137). Please contact the instructor if you have not taken this module and still want to enroll.

In order to be eligible to select this module, students must have a strong competency in mathematical and programming skills, including:

  • Fourier Theory (discrete and continuous; sampling; convolution);
  • Linear Algebra (Eigenvalues and Eigenvectors; Matrix Algebra)
  • Calculus (functions of multiple variables; calculus of variation)
  • Probability (Gaussian and Poisson probabilities; Bayes Theorem)
  • Matlab programming (multidimensional arrays; image visualisation; anonymous functions)

Students should take the self-test available here to assess their mathematical ability for this course


  • Introduction
    • Example problems
    • Data Fitting Concepts
    • Existence
    • Uniqueness
    • Stability
    • Bayesian interpretation
  • Mathematical Tools
    • Linear Algebra
      • Solving Systems of Linear Equations
      • Over and Under Determined Problems
      • Eigen-Analysis and SVD.
    • Variational Methods
      • Calculus of Variation
      • Multivariate Derivatives
      • Frechet and Gateaux Derivatives
    • Regulariation
      • Tikhonov and Generalised Tikhonov
      • Non-Quadratic Regularisation
      • Non-Convex Regularisation
      • Methods for selection of regularisation parameters
  • Numerical Tools
    • Descent Methods
      • Steepest Descent
      • Conjugate Gradients
      • Line Search
    • Newton Methods
      • Gauss Newton and Full Newton
      • Trust-Region and Globalisation
      • Quasi-Newton
      • Inexact Newton
  • Optimisation Methods
    • Least-Squares Problems
      • Linear Least Squares
      • Non-linear Least Squares
    • Non-Quadratic Problems
      • Poisson Likelihood
      • Kullback-Leibler Divergence
    • Lagrangian penalties and constrained optimisation
    • Proximal methods
  • Concepts of sparsity
    • L1 and total variation
    • wavelet compression
    • dictionary methods.
  • Bayesian Approach
    • Maximum Likelihood and Maximum A Posteriori estimates
    • Expectation-Minimisation
    • Posterior Sampling
      • Confidence-Limits
      • Monte Carlo Markov Chain
  • Applications
    • Image Denoising
    • Image Deblurring
    • Inpainting
    • Linear Image Reconstruction
      • Tomographic Reconstruction
      • Reconstruction from Incomplete Data
    • Non-Linear Parameter Estimation
    • General Concepts
    • Direct and Adjoint Differentiation
  • Other Approaches
    • Learning Based Methods

An indicative reading list is available via


The module is delivered through a combination of lectures, tutorials, seminars, written and programming exercises, and project work.


This module delivery is assessed as below:



Weight (%)



Coursework 2




Coursework 1




Literature critique




Project report



In order to pass this Module Delivery, students must:

  • achieve an overall weighted Module mark of at least 50.00%;

AND, when taken as part of MEng Computer Science and MEng Mathematical Computation:

  • achieve a mark of at least 40.00% in any Components of assessment weighed ≥ 30% of the module.

Where a Component comprises multiple Assessment Tasks, the minimum mark applies to the overall component.