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International Conference on Neural Information Processing

ICONIP 2019: Neural Information Processing pp 99–108Cite as

Dual-Path Recurrent Network for Image Super-Resolution

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1143))

Abstract

Recently, significant progress has been made for the task of image super-resolution (SR). However, existing methods show that stacking convolution layers blindly leads to overwhelming parameters and high computational complexities. Besides, the conventional feed-forward architectures can hardly fully exploit the mutual dependencies between low- and high-resolution images. Motivated by these observations, we first propose a novel architecture by taking advantage of recursive learning. Based on dual-path block (DPB), we enhance the network performance simply by increasing the number of DPB recursions and avoiding additional parameters. Moreover, in contrast to most convolutional neural network (CNN) methods with one state, our network is endowed with two states (low-resolution state and high-resolution state) transformed mutually. We exploit the relationship between the two states by introducing a back-projection operation which calculates the differences between the two states for a better result. Extensive experiments show that our method outperforms the state-of-the-art methods.

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Acknowledgments

This work is supported by National Natural Science Foundation of China (grants No. 61672133 and No. 61832001).

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Authors and Affiliations

  1. Center for Future Media, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Xinyao Li, Dongyang Zhang & Jie Shao

Authors
  1. Xinyao Li
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  2. Dongyang Zhang
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  3. Jie Shao
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Corresponding author

Correspondence to Jie Shao .

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Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  3. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

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Cite this paper

Li, X., Zhang, D., Shao, J. (2019). Dual-Path Recurrent Network for Image Super-Resolution. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-030-36802-9_12

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  • DOI: https://doi.org/10.1007/978-3-030-36802-9_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36801-2

  • Online ISBN: 978-3-030-36802-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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