Computational Imaging

This project will develop and analyze new algorithms for computational imaging, exploiting image structure to develop new solutions that can achieve speeds comparable or exceeding that of their classical analytic imaging counterparts while simultaneously providing improved reconstruction performance.

Current model-based image reconstruction provides the state-of-the art algorithms for reconstructing images from noisy or limited measurement data. Such techniques typically work by combining the measurement data with strong prior information on the image structure. However, the resulting optimization problems tend to be much more computationally expensive to solve than more classical analytic imaging solutions.  Recently neural network solutions appear to be able to compete with such techniques at a much-reduced complexity. However, neural networks require extensive training with large quantities of data and provide black box solutions without clear guarantees on the quality of the reconstruction. This research will take inspiration from these recent neural network solutions as well as advances in the theory of sublinear time sparse reconstruction algorithms to develop new solutions that are both fast and with performance guarantees. In the course of the work exemplar imaging applications such as electron-optic, radar and 3D/4D tomography will be considered.

The studentship forms a key component of the advanced ERC project C-SENSE, "Exploiting Low Dimensional Models in Sensing, Computation and Processing" and the successful candidate will join Prof. Davies’ research team based in the Institute for Digital Communications (IDCOM) at the University of Edinburgh.

Further Information: 

For more information on Prof Davies’ research see:

http://www.research.ed.ac.uk/portal/mdavies4

Closing Date: 

Friday, July 27, 2018
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ERC (European Research Council)

Principal Supervisor: 

Eligibility: 

The position is for EU/UK students only.  The ideal candidate should have at least an Honours degree, 2:1 or higher, in electrical engineering/computer science or applied mathematics/statistics and possibly supported by an MSc degree. The candidate should have a strong interest in signal processing and imaging.

Further information on English language requirements for EU/Overseas applicants.

Funding: 

Tuition fees + stipend are available for Home/EU students (International students are not eligible).

The studentship is 3 years in duration with an annual stipend in the first year of £ 20,360, updated in the subsequent years according to inflation. The enhanced stipend rate includes funding to cover annual tuition fees (at UK/EU rate). 

Further information and other funding options.

Informal Enquiries: