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A Lightweight Local Attention Network for Image Super-Resolution

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MultiMedia Modeling (MMM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14554))

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Abstract

For many years, deep neural networks have been used for Single Image Super-resolution (SISR) tasks. However, more extensive networks require higher computing and storage costs, obstructing their deployment on resource-constrained devices. How to ensure the model lightweight while improving its performance is an important direction of SISR research at present. This paper proposed a lightweight local attention network (LLAN) mainly composed of lightweight residual attention groups (LRAGs). LRAG contains lightweight self-calibrated residual blocks with pixel attention (LSC-PAs) and average local attention blocks (ALABs); it utilizes the advantages of the residual connection and attention mechanism. The LSC-PA with strong expression ability can propagate and fuse features better. The ALAB can combine global features and accelerate the network. Furthermore, we discussed three parts of a general SISR network: feature extraction, feature fusion, and reconstruction. Besides, we carried out extensive experiments using five benchmark datasets. The experimental results demonstrated that our method outperforms other compared state-of-the-art techniques, and a better balance between the complexity and performance of the SISR algorithms is achieved.

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Authors and Affiliations

  1. Hebei University, Baoding, 071002, Hebei, China

    Feng Chen, Xin Song & Liang Zhu

Authors
  1. Feng Chen
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  2. Xin Song
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  3. Liang Zhu
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Corresponding author

Correspondence to Liang Zhu .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Stevan Rudinac

  2. Delft University of Technology, Delft, The Netherlands

    Alan Hanjalic

  3. Delft University of Technology, Delft, The Netherlands

    Cynthia Liem

  4. University of Amsterdam, Amsterdam, The Netherlands

    Marcel Worring

  5. Reykjavik University, Reykjavik, Iceland

    Björn Þór Jónsson

  6. Microsoft Research Lab – Asia, Beijing, China

    Bei Liu

  7. The University of Tokyo, Tokyo, Japan

    Yoko Yamakata

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Chen, F., Song, X., Zhu, L. (2024). A Lightweight Local Attention Network for Image Super-Resolution. In: Rudinac, S., et al. MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14554. Springer, Cham. https://doi.org/10.1007/978-3-031-53305-1_28

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  • DOI: https://doi.org/10.1007/978-3-031-53305-1_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53304-4

  • Online ISBN: 978-3-031-53305-1

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