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Adaptive Densely Residual Network for Image Super-Resolution

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Data Science (ICPCSEE 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1451))

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Abstract

Many networks are designed to stack a large number of residual blocks, deepen the network and improve network performance through short residual connec-tion, long residual connection, and dense connection. However, without consider-ing different contributions of different depth features to the network, these de-signs have the problem of evaluating the importance of different depth features. To solve this problem, this paper proposes an adaptive densely residual net-work (ADRNet) for the single image super resolution. ADRN realizes the evalua-tion of distributions of different depth features and learns more representative features. An adaptive densely residual block (ADRB) was designed, combining 3 residual blocks (RB) and dense connection was added. It learned the attention score of each dense connection through adaptive dense connections, and the at-tention score reflected the importance of the features of each RB. To further en-hance the performance of ADRB, a multi-direction attention block (MDAB) was introduced to obtain multi-directional context information. Through comparative experiments, it is proved that theproposed ADRNet is superior to the existing methods. Through ablation experiments, it is proved that evaluating features of different depths helps to improve network performance.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Heilongjiang University, Harbin, China

    Wen Zhao

Authors
  1. Wen Zhao
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Corresponding author

Correspondence to Wen Zhao .

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Editors and Affiliations

  1. North University of China, Taiyuan, China

    Jianchao Zeng

  2. North University of China, Taiyuan, China

    Pinle Qin

  3. Northeast Forestry University, Harbin, China

    Weipeng Jing

  4. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  5. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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Zhao, W. (2021). Adaptive Densely Residual Network for Image Super-Resolution. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1451. Springer, Singapore. https://doi.org/10.1007/978-981-16-5940-9_25

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  • DOI: https://doi.org/10.1007/978-981-16-5940-9_25

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-5939-3

  • Online ISBN: 978-981-16-5940-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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