About the project
In X-ray tomography, limitations in measurement often result in increased image noise and artifacts; to address this, advanced machine learning techniques have been suggested. This project will investigate the application of deep equilibrium models in practical X-ray imaging scenarios, aiming to create tools for analysing large real-world datasets.
Machine learning has revolutionised many scientific fields over recently years and has become an increasingly useful tools in a wide range of image processing applications; including in X-ray tomography. X-ray tomography is an imaging technique that utilises X-ray radiation to generate a three-dimensional image of internal object structures. The technique is thus used routinely in medical diagnostics, security screening as well as scientific investigations.
In many X-ray tomography applications, constraints on the imaging process mean that we are often only able to collect limited X-ray measurements, which can lead to significant image noise and artefacts. Many advanced machine learning methods have thus been proposed to reduce these errors. In this project, you will be exploring the use of the recently introduced deep equilibrium models. In particular, the focus will be on the use of these methods in realistic X-ray imaging settings with the goal to develop tools that can be readily applied to large real datasets.
To achieve these goals, several challenges need to be addressed such as the development of efficient methods that can cope with realistically sized image data and the limited training data that is typically available.
Whilst the project will be predominately computational, there will also be the chance to work closely with the University of Southampton’s dedicated X-ray Computed Tomography (X-CT) centre “µ-VIS”, which is part of the UK’s National facility for X-CT. The centre houses some of the UK’s largest micro-focus CT scanning systems with the capability to unveil sub-surface information from an extremely wide range of materials, components and structures. With strong links between both research and industry, the centre is used for an extensive list of applications which will offer many opportunities to apply your innovations directly to a host of relevant scientific and industrial imaging challenges.
