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An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation Networks

  • Kangfu Mei
  • Aiwen Jiang
  • Juncheng Li
  • Jihua Ye
  • Mingwen Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)

Abstract

Recent works on single-image super-resolution are concentrated on improving performance through enhancing spatial encoding between convolutional layers. In this paper, we focus on modeling the correlations between channels of convolutional features. We present an effective deep residual network based on squeeze-and-excitation blocks (SEBlock) to reconstruct high-resolution (HR) image from low-resolution (LR) image. SEBlock is used to adaptively recalibrate channel-wise feature mappings. Further, short connections between each SEBlock are used to remedy information loss. Extensive experiments show that our model can achieve the state-of-the-art performance and get finer texture details.

Keywords

Single image super resolution Squeeze-and-excitation block Channel-wise recalibrate Deep residual learning Image restoration 

Notes

Acknowledgment

This work was supported by National Natural Science Foundation of China under Grant Nos. 61365002, 61462042 and 61462045.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Kangfu Mei
    • 1
  • Aiwen Jiang
    • 1
  • Juncheng Li
    • 2
  • Jihua Ye
    • 1
  • Mingwen Wang
    • 1
  1. 1.School of Computer Information EngineeringJiangxi Normal UniversityNanchangChina
  2. 2.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina

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