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International Conference on Intelligent Information Processing

IIP 2022: Intelligent Information Processing XI pp 457–472Cite as

Super-Resolution of Defocus Thread Image Based on Cycle Generative Adversarial Networks

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Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 643))

Abstract

The dual camera calibration measurement method can realize low-cost and high-precision bolt dimension measurement by using two microscope cameras. But the height difference between the thread crest and root exceeds the depth of field, and the thread image becomes defocus, which seriously affects the measurement accuracy. For this reason, a super-resolution method for defocus thread image based on cyclic generative adversarial networks is proposed. We collected focus thread images and defocus thread images as training data. Two encoders are used in the generation network to extract image defocus features and content features. And a sub-pixel convolution layer is added to the decoder to achieve image super-resolution. A loss function based on adversarial loss and cycle-consistent loss is constructed to realize unsupervised training of the network, thereby achieve super-resolution of defocus thread images. The experimental results show that, in the simulated defocus images, the method has superiority in image detail preservation, sharpness improvement and peak signal to noise ratio. In the bolt dimension measurement task, it can effectively reconstruct the clear thread image and thus provide the measurement accuracy to 0.01 mm.

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Funding

This research was supported by the Department of Science & Technology of Shandong Province (2017CXGC0810).

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Authors and Affiliations

  1. School of Information Science and Engineering, University of Jinan, Jinan, 250022, China

    Pengfei Jiang, Wangqing Xu & Jinping Li

  2. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, 250022, China

    Pengfei Jiang, Wangqing Xu & Jinping Li

  3. Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-Year, Jinan, 250022, China

    Pengfei Jiang, Wangqing Xu & Jinping Li

Authors
  1. Pengfei Jiang
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  2. Wangqing Xu
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  3. Jinping Li
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Corresponding author

Correspondence to Jinping Li .

Editor information

Editors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Zhongzhi Shi

  2. IRD, Sorbonne University, Bondy, France

    Jean-Daniel Zucker

  3. Nanyang Technological University, Singapore, Singapore

    Bo An

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© 2022 IFIP International Federation for Information Processing

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Jiang, P., Xu, W., Li, J. (2022). Super-Resolution of Defocus Thread Image Based on Cycle Generative Adversarial Networks. In: Shi, Z., Zucker, JD., An, B. (eds) Intelligent Information Processing XI. IIP 2022. IFIP Advances in Information and Communication Technology, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-031-03948-5_37

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  • DOI: https://doi.org/10.1007/978-3-031-03948-5_37

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-03947-8

  • Online ISBN: 978-3-031-03948-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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