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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Summary: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 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. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2015.2439281