A Deep Learning Approach for Underwater Image Enhancement

  • Javier PerezEmail author
  • Aleks C. Attanasio
  • Nataliya Nechyporenko
  • Pedro J. Sanz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10338)


Image processing in underwater robotics is one of the most challenging problems in autonomous underwater robotics due to light transmission in water. Although image restoration techniques are able to correctly remove the haze in a degraded image they need many images from the same location making impossible to use it in a real time system. Taking into account the great results of deep learning techniques in other image processing problems such as colorizing images or detecting objects a deep learning solution is proposed. A convolutional neural network is trained with image restoration techniques to dehaze single images outperforming other image enhancement techniques. The proposed approach is able to produce image restoration quality images with a single image as input. The neural network is validated using images from different locations and characteristics to prove the generalization capabilities.


Underwater robotics Deep learning Image dehazing 



This work has been partially funded by Spanish Ministry under grant DPI2014-57746-C3 (MERBOTS Project), Generalitat Valenciana grant PROMETEO/2016/066 and Universitat Jaume I grant PREDOC/2012/47. The authors would like to acknowledge the Australian Centre for Field Robotics’ marine robotics group for providing the data used in this work.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Javier Perez
    • 1
    Email author
  • Aleks C. Attanasio
    • 1
  • Nataliya Nechyporenko
    • 1
  • Pedro J. Sanz
    • 1
  1. 1.Department of Computer Science and EngineeringJaume I UniversityCastellón de la PlanaSpain

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