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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Learning a Deep Convolutional Network for Image Super-Resolution
verfasst von : Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang
Erschienen in: Computer Vision – ECCV 2014
Verlag: Springer International Publishing
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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.