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
Klatzer T
(2024)
Accelerated Bayesian Imaging by Relaxed Proximal-Point Langevin Sampling
in SIAM Journal on Imaging Sciences
Li Z
(2022)
Sparse Linear Spectral Unmixing of Hyperspectral Images Using Expectation-Propagation
in IEEE Transactions on Geoscience and Remote Sensing
Maria Sanz-Serna Jesus
(2021)
Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations
in JOURNAL OF MACHINE LEARNING RESEARCH
Melidonis S
(2024)
Empirical Bayesian Imaging With Large-Scale Push-Forward Generative Priors
in IEEE Signal Processing Letters
Melidonis S
(2025)
Score-Based Denoising Diffusion Models for Photon-Starved Image Restoration Problems
in Transactions on Machine Learning Research
Melidonis S
(2023)
Efficient Bayesian Computation for Low-Photon Imaging Problems
in SIAM Journal on Imaging Sciences
Pereyra M
(2023)
The Split Gibbs Sampler Revisited: Improvements to Its Algorithmic Structure and Augmented Target Distribution
in SIAM Journal on Imaging Sciences
Tarpau C
(2024)
Statistical modelling and Bayesian inversion for a Compton imaging system: application to radioactive source localization
in Inverse Problems
| 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 |