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Image Super-Resolution Using Deep Convolutional Networks.
- Source :
- IEEE Transactions on Pattern Analysis & Machine Intelligence; Feb2016, Vol. 38 Issue 2, p295-307, 13p
- Publication Year :
- 2016
-
Abstract
- 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. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 01628828
- Volume :
- 38
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Pattern Analysis & Machine Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 112245839
- Full Text :
- https://doi.org/10.1109/TPAMI.2015.2439281