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 Faccio, Professor Daniele and Tonolini, Francesco and Radford, Jack and Turpin, Dr Alejandro |
| 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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