Adaptive retinex back-light compensation algorithm using skewness information of image
Pages 165 - 170
Abstract
This paper presents an adaptive Retinex algorithm. In order to solve the typical problems of the Retinex algorithm, such as expensive computational cost, halo artifact, and color distortion, a function of skewness that represents a statistical distribution of pixels is defined to compensate for contrast and color distortion. The experimental results show that the proposed algorithm leads to subjectively better performance than the typical Retinex algorithm, and that proposed algorithm also has the capability of reducing the computational cost by approximately 40%.
References
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B. Funt, F, Ciurea, and J, McCann, "Retinex in MATLAB", Journal of Electronic Imaging, Vol. 13, No. 1, pp. 48--57, 2004
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E. Land and J. McCann, "Lightness and retinex theory", Journal of the optical Society of America, vol.61, No.1, pp. 1--11, January 1971.
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Z. Rahman, G. A. Woodell, and D. J. Jobson, "A comparison of the multi scale retinex with other image enhancement techniques", NASA Langley Technical Report, 1997.
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T. Watanabe, Y. Kuwahara, and T. Kurosawa, "An adaptive multi-Scale retinex algorithm realizing high color quality and high-speed processing", Journal of Imaging Science and Technology, Vol.49, No.5, pp.486--497, 2005.
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L. Wang, T. Horiuchi, and H. Kotera, "High dynamic range image compression by fast integrated surround retinex model", Journal of Imaging Science and Technology, Vol.51, No.1, pp.34--43, 2005.
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D. J. Jobson, Z. Rahman, and G. A. Woodell, "A multi-scale retinex for bridging the gap between color images and the human observation of scenes", IEEE Transaction on Image Processing, Vol.6, No.7, pp.965--976, July 1997.
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Index Terms
- Adaptive retinex back-light compensation algorithm using skewness information of image
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Published In
October 2013
529 pages
ISBN:9781450323482
DOI:10.1145/2513228
- Conference Chairs:
- Ching Y. Suen,
- Amir Aghdam,
- Minyi Guo,
- Program Chairs:
- Jiman Hong,
- Esmaeil Nadimi
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Published: 01 October 2013
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RACS'13
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RACS '13 Paper Acceptance Rate 73 of 317 submissions, 23%;
Overall Acceptance Rate 393 of 1,581 submissions, 25%
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