Color Correction of Underwater Images for Aquatic Robot Inspection

  • Luz A. Torres-Méndez
  • Gregory Dudek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3757)


In this paper, we consider the problem of color restoration using statistical priors. This is applied to color recovery for underwater images, using an energy minimization formulation. Underwater images present a challenge when trying to correct the blue-green monochrome look to bring out the color we know marine life has. For aquatic robot tasks, the quality of the images is crucial and needed in real-time. Our method enhances the color of the images by using a Markov Random Field (MRF) to represent the relationship between color depleted and color images. The parameters of the MRF model are learned from the training data and then the most probable color assignment for each pixel in the given color depleted image is inferred by using belief propagation (BP). This allows the system to adapt the color restoration algorithm to the current environmental conditions and also to the task requirements. Experimental results on a variety of underwater scenes demonstrate the feasibility of our method.


Color Space Training Image Belief Propagation Markov Random Field Image Patch 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Luz A. Torres-Méndez
    • 1
  • Gregory Dudek
    • 1
  1. 1.Centre for Intelligent MachinesMcGill UniversityMontrealCA

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