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Wide Activation Fourier Channel Attention Network for Super-Resolution

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Digital Multimedia Communications (IFTC 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2066))

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Abstract

Attention mechanisms, especially channel attention, have been widely used in a wide range of tasks in computer vision. More recently, researchers have begun to apply channel attention mechanisms to tasks involving single image super-resolution (SISR). However, these mechanisms, borrowed from other computer vision tasks, may not be well-suited for SISR, which primarily focuses on re-covering high-frequency information. Consequently, existing approaches may not adequately reconstruct high-frequency details. To address this limitation, we propose a novel channel attention block, i.e., the Fourier channel attention block (FCA). This block leverages the Fourier transform to extract high-frequency information and subsequently compresses the spatial information, thereby emphasizing the high-frequency components within the image. To further enhance the performance, we propose a wide activation Fourier channel attention super-resolution network (WFCASR) to enhance the residual block by incorporating the wide activation mechanism and FCA. Results in the development of. By integrating the FCA block and the wide activation mechanism into our network, the high-frequency information can be effectively reconstructed and thus the accuracy and effectiveness of SISR can be effectively improved. Experimental results demonstrated that Our FCA channel attention mechanism has better performance.

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Authors and Affiliations

  1. Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China

    Xuan Wu, Ming Tan, Liang Chen & Yi Wu

Authors
  1. Xuan Wu
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  2. Ming Tan
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  3. Liang Chen
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  4. Yi Wu
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Corresponding author

Correspondence to Liang Chen .

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Editors and Affiliations

  1. Shanghai Jiao Tong Univeristy, Shanghai, China

    Guangtao Zhai

  2. Shanghai Jiao Tong University, Shanghai, China

    Jun Zhou

  3. Communication University of China, Bei**g, China

    Long Ye

  4. Shanghai Jiao Tong University, Shanghai, China

    Hua Yang

  5. Shanghai University, Shanghai, China

    ** An

  6. Shanghai Jiao Tong University, Shanghai, China

    **aokang Yang

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Wu, X., Tan, M., Chen, L., Wu, Y. (2024). Wide Activation Fourier Channel Attention Network for Super-Resolution. In: Zhai, G., Zhou, J., Ye, L., Yang, H., An, P., Yang, X. (eds) Digital Multimedia Communications. IFTC 2023. Communications in Computer and Information Science, vol 2066. Springer, Singapore. https://doi.org/10.1007/978-981-97-3623-2_5

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  • DOI: https://doi.org/10.1007/978-981-97-3623-2_5

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