Underwater Image Enhancement by Wavelength Compensation and Dehazing

Light scattering and color change are two major sources of distortion for underwater photography. Light scattering is caused by light incident on objects reflected and deflected multiple times by particles present in the water before reaching the camera. This in turn lowers the visibility and contrast of the image captured. Color change corresponds to the varying degrees of attenuation encountered by light traveling in the water with different wavelengths, rendering ambient underwater environments dominated by a bluish tone. No existing underwater processing techniques can handle light scattering and color change distortions suffered by underwater images, and the possible presence of artificial lighting simultaneously. This paper proposes a novel systematic approach to enhance underwater images by a dehazing algorithm, to compensate the attenuation discrepancy along the propagation path, and to take the influence of the possible presence of an artifical light source into consideration. Once the depth map, i.e., distances between the objects and the camera, is estimated, the foreground and background within a scene are segmented. The light intensities of foreground and background are compared to determine whether an artificial light source is employed during the image capturing process. After compensating the effect of artifical light, the haze phenomenon and discrepancy in wavelength attenuation along the underwater propagation path to camera are corrected. Next, the water depth in the image scene is estimated according to the residual energy ratios of different color channels existing in the background light. Based on the amount of attenuation corresponding to each light wavelength, color change compensation is conducted to restore color balance. The performance of the proposed algorithm for wavelength compensation and image dehazing (WCID) is evaluated both objectively and subjectively by utilizing ground-truth color patches and video downloaded from the Youtube website. Both results demonstrate that images with significantly enhanced visibility and superior color fidelity are obtained by the WCID proposed.

[1]  David Doubilet.  Light in the Sea , 2022, World Literature Today.

[2]  N Carlevaris-Bianco,et al.  Initial results in underwater single image dehazing , 2010, OCEANS 2010 MTS/IEEE SEATTLE.

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

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

[5]  E. Trucco,et al.  Self-Tuning Underwater Image Restoration , 2006, IEEE Journal of Oceanic Engineering.

[6]  Y.Y. Schechner,et al.  Recovery of underwater visibility and structure by polarization analysis , 2005, IEEE Journal of Oceanic Engineering.

[7]  Carrick Detweiler,et al.  Color-accurate underwater imaging using perceptual adaptive illumination , 2010, Auton. Robots.

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

[9]  Robby T. Tan,et al.  Visibility in bad weather from a single image , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Peter Carr,et al.  Improved Single Image Dehazing Using Geometry , 2009, 2009 Digital Image Computing: Techniques and Applications.

[11]  Emanuele Trucco,et al.  Automatic indexing of underwater survey video: algorithm and benchmarking method , 2003 .

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

[13]  Dani Lischinski,et al.  A Closed-Form Solution to Natural Image Matting , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[15]  Thor I. Fossen,et al.  Underwater Robotics , 2008, Springer Handbook of Robotics.

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

[17]  Jules S. Jaffe,et al.  Computer modeling and the design of optimal underwater imaging systems , 1990 .

[18]  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.

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

[20]  Gregory Dudek,et al.  Color Correction of Underwater Images for Aquatic Robot Inspection , 2005, EMMCVPR.

[21]  Yoav Y. Schechner,et al.  Blind Haze Separation , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[22]  P. R. Jonas,et al.  The Physics of Atmospheres (2nd Edition). By John T. Houghton. C.U.P. 1986. Pp. Xvi + 271. Hardback £27.50; Paperback £9.95 , 2007 .