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Single Image Super-Resolution with a Parameter Economic Residual-Like Convolutional Neural Network

  • Ze Yang
  • Kai Zhang
  • Yudong Liang
  • Jinjun WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10132)

Abstract

Recent years have witnessed great success of convolutional neural network (CNN) for various problems both in low and high level visions. Especially noteworthy is the residual network which was originally proposed to handle high-level vision problems and enjoys several merits. This paper aims to extend the merits of residual network, such as skip connection induced fast training, for a typical low-level vision problem, i.e., single image super-resolution. In general, the two main challenges of existing deep CNN for supper-resolution lie in the gradient exploding/vanishing problem and large amount of parameters or computational cost as CNN goes deeper. Correspondingly, the skip connections or identity mapping shortcuts are utilized to avoid gradient exploding/vanishing problem. To tackle with the second problem, a parameter economic CNN architecture which has carefully designed width, depth and skip connections was proposed. Experimental results have demonstrated that the proposed CNN model can not only achieve state-of-the-art PSNR and SSIM results for single image super-resolution but also produce visually pleasant results.

Keywords

Super-resolution Deep residual-like convolutional neural network Skip connections The mount of parameters 

Notes

Acknowledgments

This work is partially supported by National Science Foundation of China under Grant No. 61473219.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Artificial Intelligence and RoboticsXi’an Jiaotong UniversityXi’anChina
  2. 2.Harbin Institute of TechnologyHarbinChina

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