Adversarial regularizers in inverse problems

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Lunz, S 
Öktem, O 
Schönlieb, CB 

Inverse Problems in medical imaging and computer vision are traditionally solved using purely model-based methods. Among those variational regularization models are one of the most popular approaches. We propose a new framework for applying data-driven approaches to inverse problems, using a neural network as a regularization functional. The network learns to discriminate between the distribution of ground truth images and the distribution of unregularized reconstructions. Once trained, the network is applied to the inverse problem by solving the corresponding variational problem. Unlike other data-based approaches for inverse problems, the algorithm can be applied even if only unsupervised training data is available. Experiments demonstrate the potential of the framework for denoising on the BSDS dataset and for computed tomography reconstruction on the LIDC dataset.

Journal Title
NIPS'18: Proceedings of the 32nd International Conference on Neural Information Processing Systems
Conference Name
32nd Conference on Neural Information Processing Systems (NIPS 2018)
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Association for Computing Machinery
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Engineering and Physical Sciences Research Council (EP/H023348/1)
Leverhulme Trust (RPG-2015-250)
Engineering and Physical Sciences Research Council (EP/N014588/1)
Alan Turing Institute (unknown)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (777826)
Leverhulme Trust (PLP-2017-275)
The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study. The work by Sebastian Lunz was supported by the EPSRC grant EP/L016516/1 for the University of Cambridge Centre for Doctoral Training, the Cambridge Centre for Analysis and by the Cantab Capital Institute for the Mathematics of Information. The work by Ozan Öktem was supported by the Swedish Foundation for Strategic Research grant AM13-0049. Carola-Bibiane Schönlieb acknowledges support from the Leverhulme Trust project on ‘Breaking the non-convexity barrier’, EPSRC grant Nr. EP/M00483X/1, the EPSRC Centre Nr. EP/N014588/1, the RISE projects CHiPS and NoMADS, the Cantab Capital Institute for the Mathematics of Information and the Alan Turing Institute.