Deep Convolutional Neural Network for Inverse Problems in Imaging

In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but...

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Veröffentlicht in:	IEEE transactions on image processing Jg. 26; H. 9; S. 4509 - 4522
Hauptverfasser:	Jin, Kyong Hwan, McCann, Michael T., Froustey, Emmanuel, Unser, Michael
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.09.2017
Schlagworte:	biomedical imaging biomedical signal processing Computed tomography Convolution Image reconstruction Image restoration Inverse problems Iterative methods magnetic resonance imaging Neural networks reconstruction algorithms tomography
ISSN:	1057-7149, 1941-0042, 1941-0042
Online-Zugang:	Volltext
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