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Compression Reconstruction Network with Coordinated Self-Attention and Adaptive Gaussian Filtering Module

Author

Listed:
  • Zhen Wei

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Qiurong Yan

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Xiaoqiang Lu

    (Qiyuan Lab, Beijing 100095, China)

  • Yongjian Zheng

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Shida Sun

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Jian Lin

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

Abstract

Although compressed sensing theory has many advantages in image reconstruction, its reconstruction and sampling time is very long. Fast reconstruction of high-quality images at low measurement rates is the direction of the effort. Compressed sensing based on deep learning provides an effective solution for this. In this study, we propose an attention-based compression reconstruction mechanism (ACRM). The coordinated self-attention module (CSAM) is designed to be embedded in the main network consisting of convolutional blocks and utilizes the global space and channels to focus on key information and ignore irrelevant information. An adaptive Gaussian filter is proposed to solve the loss of multi-frequency components caused by global average pooling in the CSAM, effectively supplementing the network with different frequency information at different measurement rates. Finally, inspired by the basic idea of the attention mechanism, an improved loss function with attention mechanism (AMLoss) is proposed. Extensive experiments show that the ACRM outperforms most compression reconstruction algorithms at low measurement rates.

Suggested Citation

  • Zhen Wei & Qiurong Yan & Xiaoqiang Lu & Yongjian Zheng & Shida Sun & Jian Lin, 2023. "Compression Reconstruction Network with Coordinated Self-Attention and Adaptive Gaussian Filtering Module," Mathematics, MDPI, vol. 11(4), pages 1-17, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:847-:d:1060605
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    References listed on IDEAS

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    1. Chengbo Li & Wotao Yin & Hong Jiang & Yin Zhang, 2013. "An efficient augmented Lagrangian method with applications to total variation minimization," Computational Optimization and Applications, Springer, vol. 56(3), pages 507-530, December.
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