Digital Image Processing

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Color Balance and Fusion for Underwater Image Enhancement


Affiliations
1 Kingston Engineering College, Vellore, Tamil Nadu, India
2 Vivekananda College of Engineering for Women, Tiruchengode, Tamil Nadu, India
3 Excel College of Engineering and Technology, Nammakal, Tamil Nadu, India
     

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A successful method is initiated to the underwater images which get skint due to the absorption and medium scattering. Image structure’s underwater information or any hardware is not required. The two images that are derived straight from a color – compensated and white – balanced description of the original tarnished images are figured on this. The output image’s color contrast and edges are defined by these two images which tend to fusion and also their related weight maps. A multiscale fusion strategy is personalized to avoid the pointed weight map transitions that create artifacts when low frequency components are used for the reconstructed image. Our widespread qualitative and quantitative assessment proves that our superior images and videos are considered by better protection of the dark regions, better global difference and edge sharpness. The exactness of numerous image processing applications, such as image segmentation and keypoint matching are enhanced by our algorithm since it is independent on the camera settings.


Keywords

Image Fusion, Underwater Image, White Balancing.
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  • M. D. Kocak, F. R. Dalgleish, M. F. Caimi, and Y. Y. Schechner, “A focus on recent developments and trends in underwater imaging,” Marine Technol. Soc. J., vol. 42, no. 1, pp. 52– 67, 2008.

  • G. L. Foresti, “Visual inspection of sea bottom structures by an autonomous underwater vehicle,” IEEE Trans. Syst., Man, Cybern. B, Cybern, vol. 31, no. 5, pp. 691–705, Oct. 2001.

  • A. Ortiz, M. Simó, and G. Oliver, “A vision system for an underwater cable tracker,” Mach. Vis. Appl., vol. 13, pp. 129–140, Jul. 2002.

  • A. Olmos and E. Trucco, “Detecting man-made objects in unconstrained subsea videos,” in Proc. BMVC, Sep.2002, pp. 1–10.

  • B. A. Levedahl and L. Silverberg, “Control of underwater vehicles in full unsteady flow,” IEEE J. Ocean. Eng., vol. 34, no. 4, pp. 656–668, Oct. 2009.

  • R. Schettini and S. Corchs, “Underwater image processing: state of the art of restoration and image enhancement methods,” EURASIP J. Adv. Signal Process., vol. 2010, Dec. 2010, Art. no. 746052.

  • S. G. Narasimhan and S. K. Nayar, “Contrast restoration of weather degraded images,” IEEE Trans. Pattern Anal. Mach. Learn., vol. 25, no. 6, pp. 713–724, Jun. 2003.

  • D.-M. He and G. G. L. Seet, “Divergent-beam LiDAR imaging in turbid water,” Opt. Lasers Eng., vol. 41, pp. 217–231, Jan. 2004.

  • B. L. McGlamery, “A computer model for underwater camera systems,”Proc. SPIE, vol. 208, pp. 221–231, Oct. 1979.

  • J. S. Jaffe, “Computer modeling and the design of optimal underwater imaging systems,” IEEE J. Ocean. Eng., vol. 15, no. 2, pp. 101–111, Apr. 1990.


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  • Color Balance and Fusion for Underwater Image Enhancement

Abstract Views: 355  |  PDF Views: 1

Authors

R. Dhivya
Kingston Engineering College, Vellore, Tamil Nadu, India
R. Prakash
Vivekananda College of Engineering for Women, Tiruchengode, Tamil Nadu, India
M. R. Mohanraj
Excel College of Engineering and Technology, Nammakal, Tamil Nadu, India

Abstract


A successful method is initiated to the underwater images which get skint due to the absorption and medium scattering. Image structure’s underwater information or any hardware is not required. The two images that are derived straight from a color – compensated and white – balanced description of the original tarnished images are figured on this. The output image’s color contrast and edges are defined by these two images which tend to fusion and also their related weight maps. A multiscale fusion strategy is personalized to avoid the pointed weight map transitions that create artifacts when low frequency components are used for the reconstructed image. Our widespread qualitative and quantitative assessment proves that our superior images and videos are considered by better protection of the dark regions, better global difference and edge sharpness. The exactness of numerous image processing applications, such as image segmentation and keypoint matching are enhanced by our algorithm since it is independent on the camera settings.


Keywords


Image Fusion, Underwater Image, White Balancing.

References