Underwater Image Enhancement: Using Wavelength Compensation and Image Dehazing (WCID)

Underwater environments often cause color scatter and color cast during photography. Color scatter is caused by haze effects occurring when light reflected from objects is absorbed or scattered multiple times by particles in the water. This in turn lowers the visibility and contrast of the image. Color cast is caused by the varying attenuation of light in different wavelengths, rendering underwater environments bluish. To address distortion from color scatter and color cast, this study proposes an algorithm to restore underwater images that combines a dehazing algorithm with wavelength compensation (WCID). Once the distance between the objects and the camera was estimated using dark channel prior, the haze effects from color scatter were removed by the dehazing algorithm. Next, estimation of the photography scene depth from the residual energy ratios of each wavelength in the background light of the image was performed. According to the amount of attenuation of each wavelength, reverse compensation was conducted to restore the distortion from color cast. An underwater video downloaded from the Youtube website was processed using WCID, Histogram equalization, and a traditional dehazing algorithm. Comparison of the results revealed that WCID simultaneously resolved the issues of color scatter and color cast as well as enhanced image contrast and calibrated color cast, producing high quality underwater images and videos.

[1]  R. A. Salam,et al.  Underwater Image Enhancement Using an Integrated Colour Model , 2007 .

[2]  Raanan Fattal,et al.  Single image dehazing , 2008, ACM Trans. Graph..

[3]  S. Duntley.  Light in the Sea , 1963 .

[4]  M. V. Rossum,et al.  Multiple scattering of classical waves: microscopy, mesoscopy, and diffusion , 1998, cond-mat/9804141.

[5]  Yoav Y. Schechner,et al.  Clear underwater vision , 2004, CVPR 2004.

[6]  Junku Yuh,et al.  Self-adaptive neuro-fuzzy systems for autonomous underwater vehicle control , 2001, Adv. Robotics.

[7]  Atsushi Yamashita,et al.  Color Registration of Underwater Images for Underwater Sensing with Consideration of Light Attenuation , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[8]  Weilin Hou,et al.  Automated underwater image restoration and retrieval of related optical properties , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[9]  W. L. Webb,et al.  The physics of atmospheres , 1980 .

[10]  Ashley William Gunter,et al.  Getting it for free: Using Google earth™ and IL WIS to map squatter settlements in Johannesburg , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[11]  Yoav Y. Schechner,et al.  Clear underwater vision , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[12]  Dani Lischinski,et al.  A Closed-Form Solution to Natural Image Matting , 2008 .

[13]  W. McFarland,et al.  Light in the Sea—Correlations with Behaviors of Fishes and Invertebrates , 1986 .

[14]  J. Zaneveld,et al.  Robust underwater visibility parameter. , 2003, Optics express.

[15]  Anthony Lewis Brooks,et al.  SoundScapes: non-formal learning potentials from interactive VEs , 2007, SIGGRAPH '07.

[16]  Xiaoou Tang,et al.  Single Image Haze Removal Using Dark Channel Prior , 2011 .

[17]  M. Wang,et al.  Removal of water scattering , 2010, 2010 2nd International Conference on Computer Engineering and Technology.