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LMSN:a lightweight multi-scale network for single image super-resolution

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Abstract

With the development of deep learning (DL), convolutional neural networks (CNNs) have shown great reconstruction performance in single image super-resolution (SISR). However, some methods blindly deepen the networks to purchase the performance, which neglect to make full use of the multi-scale information of different receptive fields and ignore the efficiency in practice. In this paper, a lightweight SISR network with multi-scale information fusion blocks (MIFB) is proposed to fully extract information via a multiple ranges of receptive fields. The features are refined in a coarse-to-fine manner within each block. Group convolutional layers are employed in each block to reduce the number of parameters and operations. Results of extensive experiments on the benchmarks show that our method achieves better performance than the state-of-the-arts with comparable parameters and multiply–accumulate (MAC) operations.

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Acknowledgements

The research in our paper is sponsored by National Natural Science Foundation of China (No. 61701327).

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Authors and Affiliations

  1. Sichuan University, Chengdu, China

    Yiye Zou & Xiaomin Yang

  2. Faculdade de Computação Universidade Federal de Uberlândia, Uberlândia, Brazil

    Marcelo Keese Albertini

  3. National University of Science & Technology (NUST), Islamabad, Pakistan

    Farhan Hussain

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  1. Yiye Zou
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  2. Xiaomin Yang
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  3. Marcelo Keese Albertini
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  4. Farhan Hussain
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Correspondence to Xiaomin Yang.

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Zou, Y., Yang, X., Albertini, M.K. et al. LMSN:a lightweight multi-scale network for single image super-resolution. Multimedia Systems 27, 845–856 (2021). https://doi.org/10.1007/s00530-020-00720-2

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