Convex Regularization Behind Neural Reconstruction

Neural networks have shown tremendous potential for reconstructing high-resolution images in inverse problems. The non-convex and opaque nature of neural networks, however, hinders their utility in sensitive applications such as medical imaging. To cope with this challenge, this paper advocates a co...

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Veröffentlicht in:	arXiv.org 2020-12
Hauptverfasser:	Sahiner, Arda, Mardani, Morteza, Batu Ozturkler, Pilanci, Mert, Pauly, John
Format:	Artikel
Sprache:	eng
Schlagworte:	Convexity Image reconstruction Image resolution Inverse problems Medical imaging Network management systems Neural networks Noise reduction Optimization Regularization Training
Online-Zugang:	Volltext
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