Bayesian computation for low-photon imaging
Lead Research Organisation:
University of Edinburgh
Department Name: Sch of Mathematics
Abstract
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Organisations
People |
ORCID iD |
| Konstantinos Zygalakis (Principal Investigator) |
Publications
Di Giovacchino S
(2021)
A Hierarchy of Network Models Giving Bistability Under Triadic Closure
Di Giovacchino S
(2024)
Backward error analysis and the qualitative behaviour of stochastic optimization algorithms: Application to stochastic coordinate descent
in Journal of Computational Dynamics
Di Giovacchino S
(2022)
A Hierarchy of Network Models Giving Bistability Under Triadic Closure
in Multiscale Modeling & Simulation
Eftekhari A
(2023)
The forward-backward envelope for sampling with the overdamped Langevin algorithm
in Statistics and Computing
Gong X
(2023)
Generative hypergraph models and spectral embedding.
in Scientific reports
Gong X
(2022)
Generative Hypergraph Models and Spectral Embedding
Li Z
(2022)
Sparse Linear Spectral Unmixing of Hyperspectral Images Using Expectation-Propagation
in IEEE Transactions on Geoscience and Remote Sensing
Melidonis S
(2024)
Empirical Bayesian Imaging With Large-Scale Push-Forward Generative Priors
in IEEE Signal Processing Letters
| Description | Modern imaging and computer vision systems are increasingly required to operate in extreme conditions (\eg, ultra-fast acquisition times, low illumination, long-range, unconventional environments). This has led to the development of new quantum-enhanced sensors and cameras that exploit the particle nature of light to exceed the limitations of classical imaging strategies. Unfortunately, the raw images produced by these new cameras are of very poor quality. We have developed new mathematics and algorithms to significantly enhance the quality of these images. Following some successful preliminary experiments, we are currently investigating the application of this new mathematics and algorithms to real data. |
| Exploitation Route | We anticipate that future research will specialise the proposed algorithms for specific applications of quantum-enhanced imaging, and subsequently integrate them within new imaging systems and software. |
| Sectors | Aerospace Defence and Marine Agriculture Food and Drink Chemicals Energy Environment Healthcare |