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...

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Bibliographic Details

Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 38; no. 2; pp. 295 - 307
Main Authors:	Chao Dong, Loy, Chen Change, Kaiming He, Xiaoou Tang
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.02.2016
Subjects:	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 Access:	Get full text
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