EPSRC logo

Details of Grant 

EPSRC Reference: EP/X028860/1
Title: New proximal algorithms for computational imaging: From optimisation theory to enhanced deep learning
Principal Investigator: Repetti, Dr A
Other Investigators:
Researcher Co-Investigators:
Project Partners:
University of Glasgow
Department: S of Mathematical and Computer Sciences
Organisation: Heriot-Watt University
Scheme: New Investigator Award
Starts: 01 June 2023 Ends: 31 May 2025 Value (£): 284,313
EPSRC Research Topic Classifications:
Artificial Intelligence Fundamentals of Computing
Numerical Analysis
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
28 Feb 2023 EPSRC Mathematical Sciences Prioritisation Panel February 2023 Announced
Summary on Grant Application Form
Reliable data-driven decision-making processes depend on the robustness of the methods used to interpret the data. For many applications, ranging from healthcare to astronomy, defence and finance, data interpretation consists of solving an inverse problem, by estimating an unknown object from degraded measurements (e.g., a brain image from an MR scan), that becomes even more challenging for high dimensional data. A classical approach is to define the unknown object as a solution to a minimisation problem. Such problems can be solved efficiently using optimisation algorithms, most of them having well established theoretical guarantees. Their theoretical analysis are often complex, involving tools as convex, nonconvex, stochastic optimisation theories, and monotone operator theory. Recently, growing interest has been given to optimisation methods involving NNs. Two main classes can be distinguished: PnP algorithms injecting NNs in iterative algorithms, and unfolded NNs unrolling finite number of iterations of an algorithm. Although these approaches have been shown to produce high quality results, their theoretical behavior is still not fully understood.

This project will provide new hybrid optimisation methods involving NNs, with theoretical results, to accurately solve high dimensional inverse problems. To this aim, averaging properties of unfolded NNs will be investigated, and the resulting NNs will be plugged into proximal algorithms leading to convergent PnP methods. Characterisation of the resulting method outputs will be investigated. The new algorithms will be used for computational imaging. We will particularly focus on two photon imaging applications in medicine.
Key Findings
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Potential use in non-academic contexts
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Impacts
Description This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Summary
Date Materialised
Sectors submitted by the Researcher
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Project URL:  
Further Information:  
Organisation Website: http://www.hw.ac.uk