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A Synthesis-by-Analysis Network with Applications in Image Super-Resolution

  • Lechao Cheng
  • Zhangye WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11542)

Abstract

Recent studies have demonstrated the successful application of convolutional neural networks in single image super-resolution. In this paper, we present a general synthesis-by-analysis network for super-resolving a low-resolution image. Unlike Laplacian Pyramid Super-Resolution Network (LapSRN) that progressively reconstructs the sub-band residuals of high-resolution images, our proposed network breaks through the sequential dependency to expand the input and output into multiple disjoint bandpass signals. At each band, we perform the nonlinear mapping in truncated frequency interval by applying a carefully designed sub-network. Specifically, we propose a validated network sub-structure that considers both efficiency and accuracy. We also perform exhaustive experiments in existing commonly used dataset. The recovered high-resolution image is competitive or even superior in quality compared to those images produced by other methods.

Keywords

Super-resolution Synthesis-by-analysis Bandpass 

Notes

Acknowledgements

This research work was supported partially by National Key R&D Program of China under grant No. 2017YFB1002703, Natural Science Foundation of China under Grant No. U1736109 and 863 Program of China under Grant No. 2015AA016404.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.State Key Lab of CAD&CGZhejiang UniversityHangzhouPeople’s Republic of China

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