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Journal of Real-Time Image Processing

, Volume 10, Issue 2, pp 207–217 | Cite as

A simple gray-edge automatic white balance method with FPGA implementation

  • Xin TanEmail author
  • Shiming Lai
  • Bin Wang
  • Maojun Zhang
  • Zhihui Xiong
Special Issue

Abstract

Automatic white balance is one of the most important functions in digital cameras. In addition to effectively correct color bias, the automatic white balance technology should also be fit for resource-constrained hardware and meet its real-time requirements. Based on gray-edge hypothesis, this paper proposes a simple automatic white balance method using image horizontal down-sampling with the averaging filter and horizontal first-order difference on Bayer image, and discusses its algorithm flow on FPGA. The test results show that the proposed method can correct image color bias powerfully. Moreover, the analysis of resource usage on FPGA indicates that the method consumes less hardware resource and achieves high real-time capability, and its parameter selection is unrelated with resource consumption.

Keywords

Automatic white balance Gray-edge Real-time capability FPGA 

Notes

Acknowledgments

The author is grateful to Dr. Yu Liu for stimulating discussions. This work was supported in part by the National Natural Science Foundation (NSFC) of China under Grant No.61175006, No.61175015, No. 60803101 and No.60872150.

References

  1. 1.
    Land, E.: The retinex theory of color vision. Sci. Am. 237(6), 108–128 (1977)CrossRefGoogle Scholar
  2. 2.
    Buchsbaum, G.: A spatial processor model for object colour perception. J. Frankl. Inst. 310(1), 1–26 (1980)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Van, D.W.J., Gevers, T., Gijsenij, A.: Edge-based color constancy. IEEE Trans. Image Process. 16(9), 2207–2214 (2007)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Finlayson, G., Schaefer, G.: Solving for colour constancy using a constrained dichromatic reflection model. Int. J. Comput. Vis. 42(3), 127–144 (2001)CrossRefzbMATHGoogle Scholar
  5. 5.
    Forsyth, D.: A novel algorithm for color constancy. Int. J. Comput. Vis. 5(1), 5–36 (1990)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Finlayson, G., Hordley, S.: Gamut constrained illumination estimation. Int. J. Comput. Vis. 67(1), 93–109 (2006)CrossRefGoogle Scholar
  7. 7.
    Mosny, M., Funt, B.: Cubical gamut mapping colour constancy. In: Proceedings of IS&T Fifth European Conference on Color in Graphics, Imaging and Vision, Joensuu (2010)Google Scholar
  8. 8.
    Cardei, V., Funt, B., Barnard, K.: Estimating the scene illumination chromaticity using a neural network. J. Opt. Soc. Am. A. 19(12), 2374–2386 (2002)CrossRefGoogle Scholar
  9. 9.
    Wang, N., Xu, D., Li, B.: Edge-based color constancy via support vector regression. IEICE Trans. Inf. Syst. 92(11), 2279–2282 (2009)CrossRefGoogle Scholar
  10. 10.
    Gijsenij, A., Gevers, T.: Color constancy using natural image statistics and scene semantics. IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 687–698 (2011)CrossRefGoogle Scholar
  11. 11.
    Bianco, S., Ciocca, G., Cusano, C., Schettini, R.: Improving color constancy using indoor-outdoor image classification. IEEE Trans. Image Process. 17(12), 2381–2392 (2008)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Barnard, K., Martin, L., Coath, A., Funt, B.: A comparison of computational color constancy algorithms; part ii: experiments with image data. IEEE Trans. Image Process. 11(9), 985–996 (2002)CrossRefGoogle Scholar
  13. 13.
    Li, B., Xu, D., Xiong, W., Feng, S.: Color constancy using achromatic surface. Color Res. Appl. 35(4), 304–312 (2010)CrossRefGoogle Scholar
  14. 14.
    Huo, J., Chang, Y., Wang, J., Wei, X.: Robust automatic white balance algorithm using gray color points in images. IEEE Trans. Consum. Electron. 52(2), 541–546 (2006)CrossRefGoogle Scholar
  15. 15.
    Chen, H., Shen, C., Tsai, P.: Edge-based automatic white balancing with linear illuminant constraint. In: Proceedings of Visual Communications and Image Processing, San Jose (2007)Google Scholar
  16. 16.
    Gijsenij, A., Gevers, T., Van D.W.J.: Physics-based edge evaluation for improved color constancy. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Miami Beach, pp. 1–8 (2009)Google Scholar
  17. 17.
    Gijsenij, A., Gevers, T., Van, D.W.J.: Improving color constancy by photometric edge weighting. IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 918–929 (2012)CrossRefGoogle Scholar
  18. 18.
    Verhoeven, G.J.J.: It’s all about the format—unleashing the power of RAW aerial photography. Int. J. Remote Sens. 31(8), 2009–2042 (2010)CrossRefGoogle Scholar
  19. 19.
    Ramanath, R., Snyder, W.E., Yoo, Y., Drew, M.S.: Color image processing pipeline. IEEE Signal Process. Mag. 22(1), 34–43 (2005)CrossRefGoogle Scholar
  20. 20.
    Gijsenij, A., Gevers, T., Van, D.W.J.: Computational color constancy: survey and experiments. IEEE Trans. Image Process. 20(9), 2475–2489 (2011)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Kehtarnavaz, N., Kim, N., Gamadia, M.: Real-time auto white balancing for digital cameras using discrete wavelet transform-based scoring. J. Real-Time Image Process. 1(1), 89–97 (2006)CrossRefGoogle Scholar
  22. 22.
    Gijsenij, A., Gevers, T., Lucassen, M.P.: Perceptual analysis of distance measures for color constancy algorithms. J. Opt. Soc. Am. A. 26(10), 2243–2256 (2009)CrossRefGoogle Scholar
  23. 23.
    Gehler, P., Rother, C., Blake, A., Minka, T., Sharp, T.: Bayesian color constancy revisited. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Snowbird, pp. 1–8 (2008)Google Scholar
  24. 24.
    Finlayson, G., Trezzi, E.: Shades of gray and colour constancy. In: Proceedings of IS&T/SID 12th Color Imaging Conference, Scottsdale, pp. 37–41 (2004)Google Scholar
  25. 25.
    Gijsenij, A., Gevers, T.: Color constancy research website on illumination estimation. http://colorconstancy.com
  26. 26.
    Shi, L., Funt, B.: Re-processed version of the Gehler color constancy dataset of 568 images. http://www.cs.sfu.ca/~colour/data/

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xin Tan
    • 1
    Email author
  • Shiming Lai
    • 1
  • Bin Wang
    • 1
  • Maojun Zhang
    • 1
  • Zhihui Xiong
    • 1
  1. 1.School of Information Systems and ManagementNational University of Defense TechnologyChangshaChina

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