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A lightweight multi-scale residual network for single image super-resolution

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Abstract

Single image super-resolution (SISR) technology based on deep learning provides an effective solution for improving image resolution. However, the computational complexity brought by the deep models also makes SISR technology face challenges in practical applications. In order to overcome the limitations of hardware devices and alleviate the memory and computational overhead brought by the deep models, we propose a lightweight multi-scale residual network for SISR. In detail, we designed a dilated residual block with new channel attention module to explore multi-scale feature information with less parameter cost and aggregate them in a weighted way to enhance the discriminative ability of the network for different scales. Meanwhile, local dense cascade is utilized to make better use of hierarchical features, which further takes advantage of the multi-scale representation. Both qualitative and quantitative experiments demonstrate that the proposed model achieves superior performance with lower model complexity.

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Availability of data and material

The data that support the findings of this study are available on request from the corresponding author.

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The code of this study is available on request from the corresponding author.

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Funding

This work was supported in part by the Research Project Supported by Shanxi Scholarship Council of China (2020–111) and in part by the Fund for Shanxi ‘1331 Project’ Key Subject Construction.

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Authors and Affiliations

  1. School of Instrument and Electronics, North University of China, Taiyuan, 030051, China

    Xiaole Chen, Ruifeng Yang & Chenxia Guo

  2. Automated Test Equipment and System Engineering Technology Research Center of Shanxi Province, Taiyuan, 030051, China

    Ruifeng Yang & Chenxia Guo

Authors
  1. Xiaole Chen
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  2. Ruifeng Yang
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  3. Chenxia Guo
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Correspondence to Ruifeng Yang.

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Chen, X., Yang, R. & Guo, C. A lightweight multi-scale residual network for single image super-resolution. SIViP 16, 1793–1801 (2022). https://doi.org/10.1007/s11760-022-02136-z

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  • DOI: https://doi.org/10.1007/s11760-022-02136-z

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