TY - GEN
T1 - Learning a deep convolutional network for image super-resolution
AU - Dong, Chao
AU - Loy, Chen Change
AU - He, Kaiming
AU - Tang, Xiaoou
PY - 2014
Y1 - 2014
N2 - 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) [15] that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage.
AB - 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) [15] that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage.
KW - deep convolutional neural networks
KW - Super-resolution
UR - http://www.scopus.com/inward/record.url?scp=84906484697&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84906484697&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10593-2_13
DO - 10.1007/978-3-319-10593-2_13
M3 - Conference contribution
AN - SCOPUS:84906484697
SN - 9783319105925
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 184
EP - 199
BT - Computer Vision, ECCV 2014 - 13th European Conference, Proceedings
PB - Springer Verlag
T2 - 13th European Conference on Computer Vision, ECCV 2014
Y2 - 6 September 2014 through 12 September 2014
ER -