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Image Super-Resolution via Deep Dictionary Learning

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Image and Graphics (ICIG 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14358))

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Abstract

The method of image super-resolution reconstruction through a dictionary usually only uses a single-layer dictionary, which not only fails to extract the deep features of the image, but also the trained dictionary may be relatively large. This paper proposes a new deep dictionary learning model. First, after preprocessing the images of the training set, the dictionary is trained by the deep dictionary learning method, and the super-resolution reconstruction is performed by adjusting the anchored neighborhood regression method. The proposed algorithm is compared with several classical algorithms on the Set5 data set and Set14 data set. The visualization and quantification results show that the proposed algorithm has a good improvement in PSNR and SSIM compared with the traditional super-resolution algorithm, and effectively reduces the dictionary size and saves reconstruction time.

Supported by Natural Science Foundation of Anhui Provincial (Grant No. 2108085MF206) and National Natural Science Foundation of China (Grant No. 61976006).

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Author information

Authors and Affiliations

  1. School of Computer and Information, Anhui Normal University, Wuhu, 241002, Anhui, China

    Yi Huang, Weixin Bian, Biao Jie, Zhiqiang Zhu & Wenhu Li

  2. Anhui Provincial Key Laboratory of Network and Information Security, Wuhu, 241002, Anhui, China

    Yi Huang, Weixin Bian, Biao Jie, Zhiqiang Zhu & Wenhu Li

Authors
  1. Yi Huang
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  2. Weixin Bian
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  3. Biao Jie
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  4. Zhiqiang Zhu
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  5. Wenhu Li
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Corresponding author

Correspondence to Weixin Bian .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Huchuan Lu

  2. University of Sydney, Sydney, NSW, Australia

    Wanli Ouyang

  3. Shenzhen University, Shenzhen, China

    Hui Huang

  4. Tsinghua University, Beijing, China

    Jiwen Lu

  5. Dalian University of Technology, Dalian, China

    Risheng Liu

  6. Institute of Automation, CAS, Beijing, China

    Jing Dong

  7. University of Technology Sydney, Sydney, NSW, Australia

    Min Xu

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Huang, Y., Bian, W., Jie, B., Zhu, Z., Li, W. (2023). Image Super-Resolution via Deep Dictionary Learning. In: Lu, H., et al. Image and Graphics . ICIG 2023. Lecture Notes in Computer Science, vol 14358. Springer, Cham. https://doi.org/10.1007/978-3-031-46314-3_2

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  • DOI: https://doi.org/10.1007/978-3-031-46314-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46313-6

  • Online ISBN: 978-3-031-46314-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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