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Fusion diversion network for fast, accurate and lightweight single image super-resolution

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Abstract

In recent years, deep convolution neural network has been widely used in image super-resolution and achieved great performance. As the network becomes deeper and deeper, the accuracy of reconstruction is higher and higher. However, it also brings a large increase in the number of parameters and computational complexity, which makes the practical application more and more difficult. In this paper, we propose an efficient image super-resolution method based on fusion diversion network (FDN), where diversion and fusion block serves as the basic build module. By using the fusion and diversion mechanism, the information can be fully interactive and transferred in the network, and the expression ability of the model can be effectively improved. Extensive experimental results show that even with much fewer layers, the proposed FDN achieves the competitive results in both accuracy and speed.

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Acknowledgments

This work is supported by Young Top Talents Foundation of China Aerospace Science and Technology Group Co., Ltd. This research is funded by the National Natural Science Foundation of China grant 61773383.

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Authors and Affiliations

  1. Beijing Institute of Spacecraft System Engineering, Beijing, China

    Zheng Gu, Liping Chen, Yanhong Zheng, Tong Wang & Tieying Li

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  1. Zheng Gu
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  2. Liping Chen
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  3. Yanhong Zheng
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  4. Tong Wang
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  5. Tieying Li
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Correspondence to Zheng Gu.

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Gu, Z., Chen, L., Zheng, Y. et al. Fusion diversion network for fast, accurate and lightweight single image super-resolution. SIViP 15, 1351–1359 (2021). https://doi.org/10.1007/s11760-021-01866-w

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  • DOI: https://doi.org/10.1007/s11760-021-01866-w

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