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Learning a Deep Convolutional Network for Image Super-Resolution

  • Chao Dong
  • Chen Change Loy
  • Kaiming He
  • Xiaoou Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8692)

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) [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.

Keywords

Super-resolution deep convolutional neural networks 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chao Dong
    • 1
  • Chen Change Loy
    • 1
  • Kaiming He
    • 2
  • Xiaoou Tang
    • 1
  1. 1.Department of Information EngineeringThe Chinese University of Hong KongChina
  2. 2.Microsoft Research AsiaBeijingChina

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