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Image Super-Resolution Based on Residual Block Dense Connection

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Knowledge Science, Engineering and Management (KSEM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12817))

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Abstract

Image super-resolution models based on convolution neural networks are facing problems such as gradient disappearance, gradient explosion, and insufficient feature utilization. This paper proposes an image super-resolution model based on feature fusion of dense connection of residual blocks. The key contributions are as follows: (1) residual block mechanism, which can make full use of the hierarchical features extracted from the residual block to alleviate the shallow feature losing. (2) In order to extract more representative key features, the feature of each level extracted from residual blocks is input into subsequent residual blocks by dense connection mechanism. (3) local feature fusion is used in a single residual block, and global feature fusion is used in the tail of the model, so that the shallow key information can be transferred to the reconstruction layer as much as possible. Empirical experiment is deployed on four benchmark test sets (Set5, Set14, Urban100 and BSDS100), the results show that both the peak signal-to-noise ratio and structural similarity are improved. (Source code: https://github.com/brown-cats/SR_RFB).

A. Gao and S. Liu—These authors contributed equally to this work.

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Acknowledgement

This paper is supported by National Natural Science Foundation of China under Grant Nos. 61502198, 61472161, 61402195, 61103091, U19A2061 and the Science and Technology Development Plan of Jilin Province under Grant No. 20160520099JH, 20150101051JC, 20190302117GX, 20180101334JC, 2019C053-3.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Juan Chen & Haiyang Jia

  2. College of Software, Jilin University, Changchun, 130012, China

    Juan Chen, Ang Gao, Siqi Liu, Haiyang Jia, Yifan Shao & Wenxin Tang

  3. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Juan Chen & Haiyang Jia

Authors
  1. Juan Chen
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  2. Ang Gao
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  3. Siqi Liu
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  4. Haiyang Jia
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  5. Yifan Shao
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  6. Wenxin Tang
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Corresponding author

Correspondence to Haiyang Jia .

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Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Han Qiu

  2. Ibaraki University, Hitachi, Japan

    Cheng Zhang

  3. University of Kentucky, Lexington, KY, USA

    Zongming Fei

  4. Texas A&M University – Commerce, Commerce, TX, USA

    Meikang Qiu

  5. Princeton University, Princeton, NJ, USA

    Sun-Yuan Kung

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Chen, J., Gao, A., Liu, S., Jia, H., Shao, Y., Tang, W. (2021). Image Super-Resolution Based on Residual Block Dense Connection. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_28

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  • DOI: https://doi.org/10.1007/978-3-030-82153-1_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82152-4

  • Online ISBN: 978-3-030-82153-1

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