13340

Image Super-Resolution Using Deep Convolutional Networks

Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang
Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong
arXiv:1501.00092 [cs.CV], (31 Dec 2014)

@article{dong2014image,

   title={Image Super-Resolution Using Deep Convolutional Networks},

   author={Dong, Chao and Loy, Chen Change and He, Kaiming and Tang, Xiaoou},

   year={2014},

   month={dec},

   archivePrefix={"arXiv"},

   primaryClass={cs.CV}

}

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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