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Deep Residual-Dense Attention Network for Image Super-Resolution

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Neural Information Processing (ICONIP 2019)

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

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Abstract

Recently, a great variety of CNN-based methods have been proposed for single image super-resolution. But how to restore more high-frequency details is still an unsolved issue. It is easy to find that the low-frequency information is similar in a pair of low-resolution and high-resolution images. So the model only needs to pay more attention to the high-frequency information to restore more realistic images which have abundant details and meet human visual system better. In this paper, we propose a deep residual-dense attention network (RDAN) for image super-resolution. Specially, we propose a channel attention module to change the weight of each channel and a spatial attention module to rescale the region weight in a channel map, which can make the model focus more on the high-frequency information. Experimental results on five benchmark datasets show that RDAN is superior to those state-of-the-art methods for both accuracy and visual performance.

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Acknowledgments

This work was supported in part by National Natural Science Foundation of China under grants 61771145 and 61371148.

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Authors and Affiliations

  1. Department of Electronic Engineering, Fudan University, Shanghai, 200433, China

    Ding Qin & Xiaodong Gu

Authors
  1. Ding Qin
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  2. Xiaodong Gu
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Corresponding author

Correspondence to Xiaodong Gu .

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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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Qin, D., Gu, X. (2019). Deep Residual-Dense Attention 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_1

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  • DOI: https://doi.org/10.1007/978-3-030-36802-9_1

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  • 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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