Abstract

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 collected a dataset of images taken in different locations with varying water properties, showing 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 our method.


Publication

The paper is availbale on arXiv.


Source Code

The code is availbale on GitHub: https://github.com/danaberman/underwater-hl.


Dataset

Download link (33GB). Includes RAW images, TIF files, camera clibration files, and distance maps.
README file.
If you use this data, please cite the paper.
To evaluate your own results, please use this evaluation code.


Scenes and Color Restoration Results

The transmission maps are displayed along with the images. They are color-mapped: warm colors indicate high values, while cold color indicate low values.
Please note that the buttons on the left switch both the image and the transmission map.
In order to increase contrast, as well as for methods by Ancuti et al., no transmission map is estimated.

RGT_3008

Restoration Methods
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True distance based on stereo
True Distances (based on stereo)
Output Transmission Map
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RGT_3158

Restoration Methods
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True distance based on stereo
True Distances (based on stereo)
Output Transmission Map
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RGT_3204

Restoration Methods
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True distance based on stereo
True Distances (based on stereo)
Output Transmission Map
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RGT_4491

Restoration Methods
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True distance based on stereo
True Distances (based on stereo)
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RGT_4485

Restoration Methods
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True distance based on stereo
True Distances (based on stereo)
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RGT_4376

Restoration Methods
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True distance based on stereo
True Distances (based on stereo)
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RGT_5450

Restoration Methods
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True distance based on stereo
True Distances (based on stereo)
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Transmission Map

RGT_5469

Restoration Methods
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True distance based on stereo
True Distances (based on stereo)
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Transmission Map

RGT_5478

Restoration Methods
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True distance based on stereo
True Distances (based on stereo)
Output Transmission Map
Transmission Map




References

[Drews et al. 2013] P. Drews, E. Nascimento, F. Moraes, S. Botelho, and M. Campos. Transmission estimation in underwater single images. In Proc. IEEE ICCV Underwater Vision Workshop, pages 825–830, 2013.
[Peng et al. 2015] Y.-T. Peng, X. Zhao, and P. C. Cosman. Single underwater image enhancement using depth estimation based on blurriness. In Proc. IEEE ICIP, 2015.
[Ancuti et al. 2016] C. Ancuti, C. O. Ancuti, C. De Vleeschouwer, R. Garcia, and A. C. Bovik. Multi-scale underwater descattering. In Proc. ICPR, 2016.
[Ancuti et al. 2017] C. O. Ancuti, C. Ancuti, C. De Vleeschouwer, L. Neumann, and R. Garcia. Color transfer for underwater dehazing and depth estimation. In Proc. IEEE ICIP, 2017(All color transfers were done with a single image).
[Emberton et al. 2017]S. Emberton, L. Chittka, and A. Cavallaro, Underwater image and video dehazing with pure haze region segmentation, Computer Vision and Image Understanding, 2017.
[Ancuti et al. 2018] . O. Ancuti, C. Ancuti, C. De Vleeschouwer, and P. Bekaert. Color balance and fusion for underwater image enhancement. IEEE Transactions on Image Processing, 27(1):379–393, 2018.