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Dense-Gated Network for Image Super-Resolution

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Abstract

Deep-learning based methods have achieved great success in the field of Single Image Super-Resolution (SISR) by progressively exploring contextual and deep semantic features. However, existing methods do not make full use of scale-space features, resulting in the restoration of high-resolution image details being blurred. In order to address this issue, the Dense-Gated Network is proposed for SISR, called DGISR, which consists of two essential blocks: Pyramid Multi-scale Extraction Block (PMEB) and Gated Attention Distillation Block (GADB). The proposed PMEB allows capturing and integrating more complex features through pyramid pooling and multi-scale operation to enhance the modeling capability of the network. The proposed GADB can extract key regions of images and reduce data redundancy, while improving the convergence of the model. The proposed DGISR outperforms other methods in visual quality and quantitative metrics on the standard benchmark datasets, as demonstrated by experimental results. Our overall method significantly outperforms the state-of-the-art methods.

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Data Availability

The available online experimental datasets in this paper are https://paperswithcode.com/.

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Acknowledgements

This work was supported in part by the Scientific Research Project of the Education Department of Liaoning Province (LJKZ0518, LJKZ0519) and the National Natural Science Foundation of China (No. 42071353).

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Author notes

  1. Jiyu Jin and Guiyue Jin contributed equally to this work.

Authors and Affiliations

  1. School of Information Science and Engineering, Dalian Polytechnic University, Dalian, 116034, China

    Shumin Fan, Tianyu Song, Pengpeng Li, Jiyu Jin & Guiyue Jin

  2. College of Information Science and Engineering, Wuchang Shouyi University, Wuhan, 430064, China

    Zhongmin Zhu

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  1. Shumin Fan
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Correspondence to Jiyu Jin or Guiyue Jin.

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Fan, S., Song, T., Li, P. et al. Dense-Gated Network for Image Super-Resolution. Neural Process Lett 55, 11845–11861 (2023). https://doi.org/10.1007/s11063-023-11399-7

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