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• W2942471690 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." @default.
• W2942471690 created "2019-05-03" @default.
• W2942471690 creator A5005343200 @default.
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• W2942471690 date "2019-04-12" @default.
• W2942471690 modified "2023-09-27" @default.
• W2942471690 title "Variational Inference for Computational Imaging Inverse Problems" @default.
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• W2942471690 hasPublicationYear "2019" @default.
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