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Residual aggregation U-shaped network for image super-resolution

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Abstract

Recent research on image super-resolution (SR) task has greatly progressed with the development of convolutional neural networks (CNNs). Most previous studies with single-scale feature enhance expressiveness by increasing network depth. However, most of them do not adequately extract and utilize multi-scale features. In this paper, we propose a novel residual aggregation U-shaped network (RAU), which fully utilizes multi-scale features to help reconstruct high-quality images. First, we use progressive downsampling structure to obtain multi-scale features and capture context information, and use progressive upsampling structure to fuse multi-scale features and fill detail texture. Second, we introduce auxiliary supervision in the middle layer to provide additional regularization and accelerate the convergence speed. Third, we propose a lightweight model for our network, and we replace the traditional convolution with the Ghost module in multiple locations of network. Extensive experiments on the challenge datasets confirmed the effectiveness of the proposed network. Our algorithm can restore high-quality high-resolution (HR) images and outperform other methods by a large margin.

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Data availability

The datasets analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the Tian** Key Laboratory of Optoelectronic Detection Technology and System Open Project[2019LODTS006]. The authors also acknowledge the anonymous reviewers for their helpful comments on the manuscript.

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Authors and Affiliations

  1. School of Computer and Information Engineering, Tian** Chengjian University, Tian**, China

    Zhenjian Yang, Peitao Yuan, Yan Zhang, Yemei Sun & Shudong Liu

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  1. Zhenjian Yang
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  2. Peitao Yuan
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  3. Yan Zhang
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  4. Yemei Sun
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  5. Shudong Liu
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Correspondence to Yan Zhang or Yemei Sun.

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Yang, Z., Yuan, P., Zhang, Y. et al. Residual aggregation U-shaped network for image super-resolution. Multimed Tools Appl 83, 58141–58158 (2024). https://doi.org/10.1007/s11042-023-14875-3

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  • DOI: https://doi.org/10.1007/s11042-023-14875-3

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