Image Super-Resolution Using Deep Convolutional Networks

We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 38; H. 2; S. 295 - 307
Hauptverfasser:	Chao Dong, Loy, Chen Change, Kaiming He, Xiaoou Tang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.02.2016
Schlagworte:	Convolutional codes deep convolutional neural networks Feature extraction Image reconstruction Image resolution Neural networks sparse coding Super-resolution Training sparse coding Super-resolution deep convolutional neural networks
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!