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Enhancing Image Rescaling Using High Frequency Guidance and Attentions in Downscaling and Upscaling Network

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Advances in Computer Graphics (CGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14495))

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Abstract

Recent image rescaling methods adopt invertible bijective transformations to model downscaling and upscaling simultaneously, where the high-frequency information learned in the downscaling process is used to recover the high-resolution image by inversely passing the model. However, less attention has been paid to exploiting the high-frequency information when upscaling. In this paper, an efficient end-to-end learning model for image rescaling, based on a newly designed neural network, is developed. The network consists of a downscaling generation sub-network (DSNet) and a super-resolution sub-network (SRNet), and learns to recover high-frequency signals. Concretely, we introduce dense attention blocks to the DSNet to produce the visually-pleasing low resolution (LR) image and model the distribution of the high-frequency information using a latent variable following a specified distribution. For the SRNet, we adapt an enhanced deep residual network by using residual attention blocks and adding a long skip connection, which transforms the predicted LR image and the random samples of the latent variable back during upscaling. Finally, we define a joint loss and adopt a multi-stage training strategy to optimize the whole network. Experimental results demonstrate that the superior performance of our model over existing methods in terms of both quantitative metrics and visual quality.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Project Nos. 62272164, 61402053), the Hunan Provincial Natural Science Foundation of China (Grant No. 2023JJ30050) and the Scientific Research Fund of Education Department of Hunan Province (Grant No. 21B0287).

Author information

Authors and Affiliations

  1. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, Hunan, China

    Yan Gui, Yan Xie, Lidan Kuang & Jin Zhang

  2. Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha, 410114, Hunan, China

    Yan Gui, Yan Xie, Lidan Kuang & Jin Zhang

  3. Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

    Zhihua Chen

Authors
  1. Yan Gui
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  2. Yan Xie
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  3. Lidan Kuang
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  4. Zhihua Chen
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  5. Jin Zhang
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Corresponding author

Correspondence to Yan Gui .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  2. Shanghai Jiao Tong University, Shanghai, China

    Lei Bi

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. MIRALab-CUI, University of Geneve, Carouge, Geneve, Switzerland

    Nadia Magnenat-Thalmann

  5. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

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Gui, Y., Xie, Y., Kuang, L., Chen, Z., Zhang, J. (2024). Enhancing Image Rescaling Using High Frequency Guidance and Attentions in Downscaling and Upscaling Network. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14495. Springer, Cham. https://doi.org/10.1007/978-3-031-50069-5_35

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  • DOI: https://doi.org/10.1007/978-3-031-50069-5_35

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  • Print ISBN: 978-3-031-50068-8

  • Online ISBN: 978-3-031-50069-5

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