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International Symposium on Parallel Architectures, Algorithms and Programming

PAAP 2020: Parallel Architectures, Algorithms and Programming pp 259–269Cite as

Enhancing Underwater Image Using Multi-scale Generative Adversarial Networks

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1362))

Abstract

Wavelength-dependent light absorption and scattering will reduce the quality of underwater images. Therefore, the characteristics of underwater images are different from those taken in natural. Low-quality underwater images affect the accuracy of pattern recognition, visual understanding, and key feature extraction in underwater scenes. In this paper, we enhance the underwater image using a multi-scale generative adversarial network with adjacent scale feature addition. Adjacent scale feature addition allows the network to more effectively capture the relevant characteristics between two image domains. The multi-scale discriminator can let the enhanced image more closer to the natural image. Our method does not rely on transmission maps and atmospheric light estimation. Experiments on a large amount of synthetic data and real data show that our method is superior to the state-of-the-art methods.

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Authors and Affiliations

  1. College of Mathematics and Computer Science, Fuzhou University, Fujian, China

    Yujie Zhang, Peixiang Chen, Jiangyi Huang & Yuzhong Chen

Authors
  1. Yujie Zhang
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  2. Peixiang Chen
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  3. Jiangyi Huang
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  4. Yuzhong Chen
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Corresponding author

Correspondence to Yuzhong Chen .

Editor information

Editors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Li Ning

  2. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Vincent Chau

  3. The University of Hong Kong, Hong Kong, China

    Francis Lau

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Zhang, Y., Chen, P., Huang, J., Chen, Y. (2021). Enhancing Underwater Image Using Multi-scale Generative Adversarial Networks. In: Ning, L., Chau, V., Lau, F. (eds) Parallel Architectures, Algorithms and Programming. PAAP 2020. Communications in Computer and Information Science, vol 1362. Springer, Singapore. https://doi.org/10.1007/978-981-16-0010-4_23

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  • DOI: https://doi.org/10.1007/978-981-16-0010-4_23

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-0009-8

  • Online ISBN: 978-981-16-0010-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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