Learned Exascale Computational Imaging (LEXCI)

Lead Research Organisation: Heriot-Watt University
Department Name: S of Mathematical and Computer Sciences

Abstract

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Publications

10 25 50
publication icon
Laumont R (2023) On Maximum a Posteriori Estimation with Plug & Play Priors and Stochastic Gradient Descent in Journal of Mathematical Imaging and Vision

publication icon
Laumont R (2022) Bayesian Imaging Using Plug & Play Priors: When Langevin Meets Tweedie in SIAM Journal on Imaging Sciences

publication icon
Tachella J (2023) Equivariant Bootstrapping for Uncertainty Quantification in Imaging Inverse Problems

 
Title PnP-ULA 
Description The Plug-and-Play unadjusted Langevin algorithm is a Markov chain Monte Carlo sampler designed for performing Bayesian inference with priors that are represented by an image denoising operator, which is typically encoded by a neural network that has been trained with application-specific data. 
Type Of Material Computer model/algorithm 
Year Produced 2022 
Provided To Others? Yes  
Impact PnP-ULA has been successfully applied to problems related to image recovery and uncertainty quantification in a range of imaging inverse problems involving synthetic data. Its application to real problems in computer tomography is currently under investigation. 
URL https://doi.org/10.1137/21M1406349
 
Title Proximal Nested Sampling 
Description Proximal Nested Sampling is a stochastic algorithm designed for computing the marginal likelihood of high-dimensional Bayesian models with an underlying convex geometry. It is based on the Moreau-Yoshida regularised unadjusted Langevin algorithm and it is useful for performing Bayesian model selection directly from observed data, without the need for ground truth data. 
Type Of Material Computer model/algorithm 
Year Produced 2022 
Provided To Others? Yes  
Impact Proximal Nested Sampling has been successfully applied to Bayesian model selection in imaging inverse problems involving synthetic data. Its application to real problems in interferometric imaging is currently under investigation. 
URL https://link.springer.com/article/10.1007/s11222-022-10152-9
 
Description Julie Delon and Andres Almansa 
Organisation University of Paris - Descartes
Country France 
Sector Academic/University 
PI Contribution We have led the development of the new Bayesian AI techniques for imaging problems where the prior knowledge is available in the form of a set of training examples.
Collaborator Contribution Julie Delon and Andres Almansa have led the machine learning aspects of the work and the algorithmic implementation of the proposed techniques.
Impact This is a multi-disciplinary collaboration at the interface of imaging sciences, applied analysis, machine learning, probability theory and mathematical statistics.
Start Year 2019
 
Description Invited tutorial at the IEEE International Conference on Image Processing, Bordeaux, Oct. 2022 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact The IEEE International Conference on Image Processing is the main global conference on image processing engineering. It is attended by over 1,000 people and has a strong presence from industry. I delivered a 3-hour tutorial masterclass at the conference on the topic of this project. I was subsequently contacted by several engineering groups who are interested in adopting the tools and techniques that I presented at the conference. Two of these groups are currently working on incorporating these tools and techniques into open-source free software libraries that they develop and make available to the image-processing engineering community.
Year(s) Of Engagement Activity 2022
URL https://2022.ieeeicip.org/