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Feature distillation network for efficient super-resolution with vast receptive field

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Abstract

In recent years, convolutional neural networks have seen rapid advancements, leading to the proposal of numerous lightweight image super-resolution techniques tailored for deployment on edge devices. This paper examines the information distillation mechanism and the vast-receptive-field attention mechanism utilized in lightweight super-resolution. Additionally, it introduces a new network structure named the vast-receptive-field feature distillation network, named VFDN, which effectively enhances inference speed and reduces GPU memory consumption. The receptive field of the attention block is expanded, and the utilization of large dense convolution kernels is substituted with depth-wise separable convolutions. Meanwhile, we modify the reconstruction block to obtain better reconstruction quality and introduce a Fourier transform-based loss function that emphasizes the frequency domain information of the input image. Experiments show that the designed VFDN achieves comparable results to RFDN, but the parameters are only 307K(55.81\(\%\) of RFDN), which is advantageous for deployment on edge devices.

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Data availability

The data used to support the findings of this study is available from the corresponding author upon request.

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Author notes

  1. Wenan Tan, Wenyi Mao have contributed equally to this work.

Authors and Affiliations

  1. School of Computer and Information Engineering, Shanghai Polytechnic University, Jinhai Road, Shanghai, 200000, China

    Yanfeng Zhang, Wenan Tan & Wenyi Mao

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  1. Yanfeng Zhang
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  2. Wenan Tan
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Contributions

Yanfeng Zhang is responsible for the conception of the model and the implementation of the experiment, and is the author of the paper. Wenan Tan is responsible for the quality control of the whole process and acts as the corresponding author of the paper. Wenyi Mao is responsible for the verification and sorting of the experimental data.

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Correspondence to Wenan Tan.

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Zhang, Y., Tan, W. & Mao, W. Feature distillation network for efficient super-resolution with vast receptive field. SIViP 19, 191 (2025). https://doi.org/10.1007/s11760-024-03750-9

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  • DOI: https://doi.org/10.1007/s11760-024-03750-9

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