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Residual shuffle attention network for image super-resolution

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Abstract

The image super-resolution reconstruction methods based on deep learning achieve satisfactory visual quality; however, the majority are difficult to be directly deployed to mobile or embedded devices due to the model complexity. This paper introduces a lightweight residual shuffle attention network for image super-resolution task. Among them, a residual shuffle attention block (RSAB) that fully integrates the information distillation mechanism is designed to extract deep features, which consists of multiple enhanced residual blocks (MERB) and shuffle attention. The MERB is capable of boosting the feature representation, and the shuffle attention can capture critical information extracted by grouping features. Furthermore, the RSAB utilizes multiple skip connection to build the module structure. Extensive experimental results have demonstrated that the network model proposed in this paper outperforms state-of-the-art methods on several benchmarks with acceptable complexity.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61876112, 61876037, 61601311), the Science and Technology Innovation Talent Project of Education Department of Henan Province (No. 23HASTIT030), and the Natural Science Funding Program of Anhui Province (No. KJ2021A0249). The authors would like to greatly appreciate the reviewers’ valued comments and suggestions which improved this article.

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Authors and Affiliations

  1. College of Information Engineering, Capital Normal University, Beijing, 100048, China

    Xuanyi Li, Zhuhong Shao & Yuanyuan Shang

  2. School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou, 450007, China

    Bicao Li

  3. School of Computer Science and Technology, Southeast University, Nanjing, 210096, China

    Jiasong Wu

  4. School of Biomedical Engineering, Anhui Medical University, Hefei, 230009, China

    Yuping Duan

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  1. Xuanyi Li
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Correspondence to Zhuhong Shao.

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Li, X., Shao, Z., Li, B. et al. Residual shuffle attention network for image super-resolution. Machine Vision and Applications 34, 84 (2023). https://doi.org/10.1007/s00138-023-01436-9

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