Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery

Recovering images from undersampled linear measurements typically leads to an ill-posed linear inverse problem, that asks for proper statistical priors. Building effective priors is however challenged by the low train and test overhead dictated by real-time tasks; and the need for retrieving visuall...

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Veröffentlicht in:	arXiv.org 2017-11
Hauptverfasser:	Mardani, Morteza, Hatef Monajemi, Papyan, Vardan, Vasanawala, Shreyas, Donoho, David, Pauly, John
Format:	Artikel
Sprache:	eng
Schlagworte:	Generative adversarial networks Ill posed problems Image reconstruction Inverse problems Magnetic resonance imaging Medical imaging Noise reduction
Online-Zugang:	Volltext
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