Variational inference for computational imaging inverse problems

Tonolini, F., Radford, J., Turpin, A. , Faccio, D. and Murray-Smith, R. (2020) Variational inference for computational imaging inverse problems. Journal of Machine Learning Research, 21(179), pp. 1-46.

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Abstract

Machine learning methods for computational imaging require uncertainty estimation to be reliable in real settings. While Bayesian models offer a computationally tractable way of recovering uncertainty, they need large data volumes to be trained, which in imaging applications implicates prohibitively expensive collections with specific imaging instruments. This paper introduces a novel framework to train variational inference for inverse problems exploiting in combination few experimentally collected data, domain expertise and existing image data sets. In such a way, Bayesian machine learning models can solve imaging inverse problems with minimal data collection efforts. Extensive simulated experiments show the advantages of the proposed framework. The approach is then applied to two real experimental optics settings: holographic image reconstruction and imaging through highly scattering media. In both settings, state of the art reconstructions are achieved with little collection of training data.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick and Turpin, Dr Alejandro and Tonolini, Francesco and Faccio, Professor Daniele and Radford, Jack
Authors: Tonolini, F., Radford, J., Turpin, A., Faccio, D., and Murray-Smith, R.
College/School:College of Science and Engineering > School of Computing Science
College of Science and Engineering > School of Physics and Astronomy
Journal Name:Journal of Machine Learning Research
Publisher:Microtome Publishing
ISSN:1532-4435
ISSN (Online):1533-7928
Copyright Holders:Copyright © 2020 Francesco Tonolini, Jack Radford, Alex Turpin, Daniele Faccio and Roderick Murray-Smith
First Published:First published in Journal of Machine Learning Research 21(179): 1-46
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
190841UK Quantum Technology Hub in Enhanced Quantum ImagingMiles PadgettEngineering and Physical Sciences Research Council (EPSRC)EP/M01326X/1P&S - Physics & Astronomy
305567QuantIC - The UK Quantum Technoogy Hub in Quantum Enhanced ImagingMiles PadgettEngineering and Physical Sciences Research Council (EPSRC)EP/T00097X/1P&S - Physics & Astronomy
300982Exploiting Closed-Loop Aspects in Computationally and Data Intensive AnalyticsRoderick Murray-SmithEngineering and Physical Sciences Research Council (EPSRC)EP/R018634/1Computing Science
190828EPSRC Centre for Doctoral Training in Sensing and MeasurementAndrew HarveyEngineering and Physical Sciences Research Council (EPSRC)EP/L016753/1P&S - Physics & Astronomy