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Cross-Scale Dynamic Alignment Network for Reference-Based Super-Resolution

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Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 14087))

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Abstract

Image super-resolution aims to recover high-resolution (HR) images from corresponding low-resolution (LR) images, but it is prone to lose significant details in reconstruction progress. Reference-based image super-resolution can produce realistic textures using an external reference (Ref) image, thus reconstructing pleasant images. Despite the remarkable advancement, there are two critical challenges in reference-based image super-resolution. One is that it is difficult to match the correspondence between LR and Ref images when they are significantly different. The other is how the details of the Ref image are accurately transferred to the LR image. In order to solve these issues, we propose improved feature extraction and matching method to find the matching relationship corresponding to the LR and Ref images more accurately, propose cross-scale dynamic correction module to use multiple scale related textures to compensate for more information. Extensive experimental results over multiple datasets demonstrate that our method is better than the baseline model on both quantitative and qualitative evaluations.

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Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grants 61976079, in part by Guangxi Key Research and Development Program under Grant AB22035022, and in part by Anhui Key Research and Development Program under Grant 202004a05020039.

Author information

Authors and Affiliations

  1. College of Computer and Information, Hefei University of Technology, Hefei, Anhui, China

    Kai Hu, Ran Chen & Zhong-Qiu Zhao

  2. Intelligent Manufacturing Institute of HFUT, Hefei, China

    Kai Hu, Ran Chen & Zhong-Qiu Zhao

Authors
  1. Kai Hu

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  2. Ran Chen

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  3. Zhong-Qiu Zhao

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Corresponding author

Correspondence toZhong-Qiu Zhao.

Editor information

Editors and Affiliations

  1. Department of Computer Science, Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Zhengzhou University of Light Industry, Zhengzhou, China

    Baohua Jin

  4. Zhong Yuan University of Technology, Zhengzhou, China

    Boyang Qu

  5. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  6. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Hu, K., Chen, R., Zhao, ZQ. (2023). Cross-Scale Dynamic Alignment Network for Reference-Based Super-Resolution. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_8

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JPY 3498
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JPY 14871
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