Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset

Dana Berman, Deborah Levy, Shai Avidan, Tali Treibitz

Research output: Contribution to journalArticlepeer-review


Underwater images suffer from color distortion and low contrast, because light is attenuated while it propagates through water. Attenuation under water varies with wavelength, unlike terrestrial images where attenuation is assumed to be spectrally uniform. The attenuation depends both on the water body and the 3D structure of the scene, making color restoration difficult. Unlike existing single underwater image enhancement techniques, our method takes into account multiple spectral profiles of different water types. By estimating just two additional global parameters: the attenuation ratios of the blue-red and blue-green color channels, the problem is reduced to single image dehazing, where all color channels have the same attenuation coefficients. Since the water type is unknown, we evaluate different parameters out of an existing library of water types. Each type leads to a different restored image and the best result is automatically chosen based on color distribution. We also contribute a dataset of 57 images taken in different locations. To obtain ground truth, we placed multiple color charts in the scenes and calculated its 3D structure using stereo imaging. This dataset enables a rigorous quantitative evaluation of restoration algorithms on natural images for the first time.

Original languageEnglish
Article number9020130
Pages (from-to)2822-2837
Number of pages16
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number8
StatePublished - 1 Aug 2021

Bibliographical note

Publisher Copyright:
© 1979-2012 IEEE.


  • Image processing and computer vision
  • computational photography
  • image color analysis
  • image enhancement
  • image restoration

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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