Diving into haze-lines: Color restoration of underwater images

Dana Berman, Tali Treibitz, Shai Avidan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Underwater images suffer from color distortion and low contrast, because light is attenuated as it propagates through water. The attenuation varies with wavelength and depends both on the properties of the water body in which the image was taken and the 3D structure of the scene, making it difficult to restore the colors. Existing single underwater image enhancement techniques either ignore the wavelength dependency of the attenuation, or assume a specific spectral profile. We propose a new method that takes into account multiple spectral profiles of different water types, and restores underwater scenes from a single image. We show that by estimating just two additional global parameters - the attenuation ratios of the blue-red and blue-green color channels - the problem of underwater image restoration can be reduced to single image dehazing, where all color channels have the same attenuation coefficients. Since we do not know the water type ahead of time, we try different parameter sets out of an existing library of water types. Each set leads to a different restored image and the one that best satisfies the Gray-World assumption is chosen. The proposed single underwater image restoration method is fully automatic and is based on a more comprehensive physical image formation model than previously used. We collected a dataset of real images taken in different locations with varying water properties and placed color charts in the scenes. Moreover, to obtain ground truth, the 3D structure of the scene was calculated based on stereo imaging. This dataset enables a quantitative evaluation of restoration algorithms on natural images and shows the advantage of the proposed method.

Original languageEnglish
Title of host publicationBritish Machine Vision Conference 2017, BMVC 2017
PublisherBMVA Press
ISBN (Electronic)190172560X, 9781901725605
StatePublished - 2017
Event28th British Machine Vision Conference, BMVC 2017 - London, United Kingdom
Duration: 4 Sep 20177 Sep 2017

Publication series

NameBritish Machine Vision Conference 2017, BMVC 2017


Conference28th British Machine Vision Conference, BMVC 2017
Country/TerritoryUnited Kingdom

Bibliographical note

Publisher Copyright:
© 2017. The copyright of this document resides with its authors.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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