COMP0028 Computational Photography and Capture

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 session2018-19
ModuleComputational Photography and Capture
Module delivery1819/A7P/T2/COMP0028 Postgraduate
Related deliveries1819/A7U/T2/COMP0028 Masters (MEng)
Prior deliveries


FHEQ LevelL7
FHEQ credits15
Term/sTerm 2
Module leaderWeyrich, Tim
ContributorsWeyrich, Tim
Module administratorHorslen, Caroline


The module is designed to be self-contained, introducing the theoretical and practical aspects of modern photography and capture algorithms to students with only limited background in visual computing. The two primary aims are i) to introduce universal models of colour, computer-controlled cameras, lighting and shape capture, and ii) to motivate students to choose among the topics presented for either continuing study (for those considering MSc’s and PhD’s) or future careers in the fields of advanced imaging.

Learning outcomes

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

  1. Understand principles of light transport in natural scenes

  2. Understand principles of digital image formation

  3. Understand how computational-photography algorithms can exploit knowledge use of these principles to transcend the capabilities of traditional photography

  4. Develop their own small software prototypes to capture and process digital images

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:
  • MRes Virtual Reality

  • MSc Computer Graphics, Vision and Imaging

  • MSc Computer Science

  • Have a good grasp of A-Level maths; and

  • A basic knowledge of MATLAB


Introduction to Computational Photography

  • Cameras, sensors and colour

  • Blending and compositing

  • Background subtraction and matting

  • Warping, morphing, mosaics and panoramas

  • High-dynamic range imaging/tome mapping

  • Hybrid images

  • Flash photography and stylised rendering using multi-flash

Image Inpainting

  • Texture synthesis

  • Image quilting

  • Heeger and Bergen

  • Simplicial complex of morphable textures (Matusik 2005)

Extension to the temporal domain

  • TIP, Video textures

  • Temporal sequence rendering

  • Ezzat speech anim, comtrolled video sprites

  • Video-based rendering: using photographs to enhance videos of a static scene

  • Motion magnification

  • Non-photorealistic rendering and animation

Colourisation and colour transfer-colorisation using optimisation

  • Colour transfer between images

  • N-Dimensional probability density function transfer and its application to colour transfer

  • Intrinsic images

  • Vectorising Raster images

  • Poisson image editing

  • Seam carving

  • De-blurring/ dehazing

  • Coded aperture imaging

Image-based rendering

  • Image-based modelling and photo editing view dependence, light-dependence, plenoptic function

  • Selected ways to capture the above representations

Extensions to the temporal domain

  • Factored time-lapse video

  • Computational time-lapse video

  • Video synopsis and indexing

Capturing images with structured light

  • Laser-stripe projection

  • ShadowCuts

  • Stripe codes

  • Edge codes

  • Phase shift

  • Brief recap of stereo, spatio-temporal stereo

  • Photometric stereo

  • The Helmholtz wheel (Helmholtz reciprocity)

Dual photography

  • Seeing around corners

  • Dual light stage

  • Separation of global and local reflectance

  • Image-based BRDF measurements

  • Measuring the BSSRDF

An indicative reading list is available via


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


This module delivery is assessed as below:

#TitleWeight (%)Notes
1Individual project60Submission takes place in two parts.
2Coursework 120 
3Coursework 220 

In order to pass this module delivery, students must achieve an overall weighted module mark of 50%.