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Unsupervised Deep-Learning Approach for Underwater Image Enhancement

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Advances in Visual Computing (ISVC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14362))

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Abstract

Underwater images often contain color casting and blurriness which reduce the quality. State-of-the-art shows different deep-learning models to handle these degradations. However, they required ground truth to train, which is impossible to acquire when studying underwater images. We present an unsupervised deep-learning approach for underwater image enhancement based on the mathematical model of a hazy image. This allows us to train networks without the need for a reference image. We use three networks to estimate the transmission map, the atmospheric light, and the enhanced image and propose a compound loss function to train our approach accurately. We achieve state-of-the-art results in the structural similarity index (SSIM) while performing optimally nearly real-time inference speeds.

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Authors and Affiliations

  1. University of Victoria, Victoria, BC, Canada

    Alejandro Rico Espinosa, Declan McIntosh & Alexandra Branzan Albu

Authors
  1. Alejandro Rico Espinosa
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  2. Declan McIntosh
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  3. Alexandra Branzan Albu
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Corresponding author

Correspondence to Alejandro Rico Espinosa .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Google Research, Mountain View, CA, USA

    Golnaz Ghiasi

  3. New York University, New York, USA

    Yi Fang

  4. Ben-Gurion University, Be'er Sheva, Israel

    Andrei Sharf

  5. Microsoft Research, Beijing, China

    Yue Dong

  6. The University of Oklahoma, Norman, OK, USA

    Chris Weaver

  7. University of Maryland, Collage Park, MD, USA

    Zhicheng Leo

  8. University of Central Florida, Orlando, FL, USA

    Joseph J. LaViola Jr.

  9. InnerOptic Technology, Hillsborough, NC, USA

    Luv Kohli

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Espinosa, A.R., McIntosh, D., Albu, A.B. (2023). Unsupervised Deep-Learning Approach for Underwater Image Enhancement. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2023. Lecture Notes in Computer Science, vol 14362. Springer, Cham. https://doi.org/10.1007/978-3-031-47966-3_18

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  • DOI: https://doi.org/10.1007/978-3-031-47966-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47965-6

  • Online ISBN: 978-3-031-47966-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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